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    "# Transforming Vertical Coordinates\n",
    "\n",
    "A common need in the analysis of ocean and atmospheric data is to transform the vertical coordinate from its original coordinate (e.g. depth) to a new coordinate (e.g. density).\n",
    "Xgcm supports this sort of one-dimensional coordinate transform on `Axis` and `Grid` objects using the `transform` method.\n",
    "Two algorithms are implemented:\n",
    "\n",
    "- _Linear interpolation:_ Linear interpolation is designed to interpolate intensive quantities (e.g. temperature) from one coordinate to another. This method is suitable when the target coordinate is monotonically increasing or decreasing and the data variable is intensive. For example, you want to visualize oxygen on density surfaces from a z-coordinate ocean model.\n",
    "- _Conservative remapping:_ This algorithm is designed to conserve extensive quantities (e.g. transport, heat content). It requires knowledge of cell bounds in both the source and target coordinate. It also handles non-monotonic target coordinates.\n",
    "\n",
    "On this page, we explain how to use these coordinate transformation capabilities."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from xgcm import Grid\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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   "cell_type": "markdown",
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    "## 1D Toy Data Example\n",
    "\n",
    "First we will create a simple, one-dimensional dataset to illustrate how the `transform` function works.\n",
    "This dataset contains\n",
    "\n",
    "- a coordinate called `z`, representing the original depth coordinate\n",
    "\n",
    "- a data variable called `theta`, a function of `z`, which we want as our new vertical coordinate\n",
    "\n",
    "- a data variable called `phi`, akso a function of `z`, which represents the data we want to transform into this new coordinate space\n",
    "\n",
    "In an oceanic context `theta` might be density and `phi` might be oxygen.\n",
    "In an atmospheric context, `theta` might be potential temperature and `phi` might be potential vorticity."
   ]
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   "cell_type": "code",
   "execution_count": 2,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (z: 10)\n",
       "Coordinates:\n",
       "  * z        (z) int64 2 3 4 5 6 7 8 9 10 11\n",
       "Data variables:\n",
       "    phi      (z) float64 1.85 1.289 1.342 1.711 ... 1.643 1.476 1.015 0.3637\n",
       "    theta    (z) float64 0.6931 1.099 1.386 1.609 ... 2.079 2.197 2.303 2.398</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0e5fc332-27bc-4573-b1cf-f539a063c20a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e5fc332-27bc-4573-b1cf-f539a063c20a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c684cb4c-762d-48de-95c3-2c5974081a24' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c684cb4c-762d-48de-95c3-2c5974081a24' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 9 10 11</div><input id='attrs-fa858cdb-8466-41fb-bff8-3d1de2d1bb7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa858cdb-8466-41fb-bff8-3d1de2d1bb7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6ab9c38-257d-427f-bff5-1f3a29fbf93d' class='xr-var-data-in' type='checkbox'><label for='data-d6ab9c38-257d-427f-bff5-1f3a29fbf93d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-702143e2-7fb5-4ee2-af6d-4028042838eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-702143e2-7fb5-4ee2-af6d-4028042838eb' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>phi</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.85 1.289 1.342 ... 1.015 0.3637</div><input id='attrs-edcd7930-a189-421a-9706-282970056721' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edcd7930-a189-421a-9706-282970056721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1d09da9-1234-4633-a23e-560090b52d40' class='xr-var-data-in' type='checkbox'><label for='data-a1d09da9-1234-4633-a23e-560090b52d40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.84972538, 1.28859294, 1.34248625, 1.71118461, 1.14369504,\n",
       "       1.83142081, 1.64334108, 1.47646734, 1.01490958, 0.36372929])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6931 1.099 1.386 ... 2.303 2.398</div><input id='attrs-b089e829-9405-46d7-b075-609501108984' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b089e829-9405-46d7-b075-609501108984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c92757f-8631-4f0b-9d9b-91f1ab7fba0a' class='xr-var-data-in' type='checkbox'><label for='data-6c92757f-8631-4f0b-9d9b-91f1ab7fba0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.69314718, 1.09861229, 1.38629436, 1.60943791, 1.79175947,\n",
       "       1.94591015, 2.07944154, 2.19722458, 2.30258509, 2.39789527])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-861d1cac-45a0-46e5-8fbf-a93d044f31e5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-861d1cac-45a0-46e5-8fbf-a93d044f31e5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (z: 10)\n",
       "Coordinates:\n",
       "  * z        (z) int64 2 3 4 5 6 7 8 9 10 11\n",
       "Data variables:\n",
       "    phi      (z) float64 1.85 1.289 1.342 1.711 ... 1.643 1.476 1.015 0.3637\n",
       "    theta    (z) float64 0.6931 1.099 1.386 1.609 ... 2.079 2.197 2.303 2.398"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.arange(2, 12)\n",
    "theta = xr.DataArray(np.log(z), dims=['z'], coords={'z': z})\n",
    "phi = xr.DataArray(np.flip(np.log(z)*0.5+ np.random.rand(len(z))), dims=['z'], coords={'z':z})\n",
    "ds = xr.Dataset({'phi': phi, 'theta': theta})\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this data. Note that, for a simple 1D profile, we can easily visualize `phi` in `theta` space by simply plotting `phi` vs. `theta`. This is essentially a form of linear interpolation, performed automatically by matplotlib when it draws lines between the discrete points of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_profile():\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=[8,5])\n",
    "    ds.theta.plot(ax=ax1, y='z', marker='.', yincrease=False)\n",
    "    ds.phi.plot(ax=ax2, y='z', marker='.', yincrease=False)\n",
    "    ds.swap_dims({'z': 'theta'}).phi.plot(ax=ax3, y='theta', marker='.', yincrease=False)\n",
    "    fig.subplots_adjust(wspace=0.5)\n",
    "    return ax3\n",
    "\n",
    "plot_profile();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear transformation\n",
    "\n",
    "Ok now lets transform `phi` to `theta` coordinates using linear interpolation.\n",
    "A key part of this is to define specific `theta` levels onto which we want to interpolate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;phi&#x27; (theta: 20)&gt;\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       1.71641693, 1.49790279, 1.28983889, 1.31941831, 1.39991707,\n",
       "       1.66080532, 1.31462947, 1.60311691, 1.68110306, 1.41819579,\n",
       "       0.5651037 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 0.0 0.1579 0.3158 0.4737 ... 2.526 2.684 2.842 3.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'phi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>theta</span>: 20</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-97a57fc6-7e3c-46c7-8049-b38e4d6714cb' class='xr-array-in' type='checkbox' checked><label for='section-97a57fc6-7e3c-46c7-8049-b38e4d6714cb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan 1.716 1.498 ... 1.681 1.418 0.5651 nan nan nan nan</span></div><div class='xr-array-data'><pre>array([       nan,        nan,        nan,        nan,        nan,\n",
       "       1.71641693, 1.49790279, 1.28983889, 1.31941831, 1.39991707,\n",
       "       1.66080532, 1.31462947, 1.60311691, 1.68110306, 1.41819579,\n",
       "       0.5651037 ,        nan,        nan,        nan,        nan])</pre></div></div></li><li class='xr-section-item'><input id='section-8b6388bb-5707-43e5-84e0-9e39c083d52f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b6388bb-5707-43e5-84e0-9e39c083d52f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta</span></div><div class='xr-var-dims'>(theta)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1579 0.3158 ... 2.842 3.0</div><input id='attrs-b5c99781-7cf7-42bc-a58e-3b62235d7710' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5c99781-7cf7-42bc-a58e-3b62235d7710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-253c0c8f-9924-4925-9f9a-a6a62e45f341' class='xr-var-data-in' type='checkbox'><label for='data-253c0c8f-9924-4925-9f9a-a6a62e45f341' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.157895, 0.315789, 0.473684, 0.631579, 0.789474, 0.947368,\n",
       "       1.105263, 1.263158, 1.421053, 1.578947, 1.736842, 1.894737, 2.052632,\n",
       "       2.210526, 2.368421, 2.526316, 2.684211, 2.842105, 3.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20d79dde-ac19-469a-92be-bff18d912390' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-20d79dde-ac19-469a-92be-bff18d912390' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'phi' (theta: 20)>\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       1.71641693, 1.49790279, 1.28983889, 1.31941831, 1.39991707,\n",
       "       1.66080532, 1.31462947, 1.60311691, 1.68110306, 1.41819579,\n",
       "       0.5651037 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 0.0 0.1579 0.3158 0.4737 ... 2.526 2.684 2.842 3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First create an xgcm grid object\n",
    "grid = Grid(ds, coords={'Z': {'center':'z'}}, periodic=False)\n",
    "\n",
    "# define the target values in density, linearly spaced\n",
    "theta_target = np.linspace(0, 3, 20)\n",
    "\n",
    "# and transform\n",
    "phi_transformed = grid.transform(ds.phi, 'Z', theta_target, target_data=ds.theta)\n",
    "phi_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see what the result looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c6fc57c40>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_profile()\n",
    "phi_transformed.plot(ax=ax, y='theta', marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad. We can increase the number of interpolation levels to capture more of the small scale structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c6fde70d0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_theta = np.linspace(0,3, 100)\n",
    "phi_transformed = grid.transform(ds.phi, 'Z', target_theta, target_data=ds.theta)\n",
    "ax = plot_profile()\n",
    "phi_transformed.plot(ax=ax, y='theta', marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that by default, values of `theta_target` which lie outside the range of `theta` have been masked (set to `NaN`).\n",
    "To disable this behavior, you can pass `mask_edges=False`; values outside the range of `theta` will be filled with the nearest valid value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c702d8790>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kEeEXgxOoqHExZX4u176UqV9cvuV/+Gnb9KSc/BI27TnEztIK/QwodSK+MgGLiIwH9hljck6xnc9OlJ+7//D3qwZU1bjIzNWZCX1BILRNT8nMLcZVO4a7slo/A0rVa/tCjMs9hNh4eR/3cOBiEcnDve7saBF57fiNfHmi/PTkKEKCbD+6rXyC37dNT0lPjiLE4f4MGKD8qK5dr9Txdhy2IYDTCBUuOxtD+zfoeR4v3MaYPxhj4o0xSbgn0//WGHOdp3M0p9TESGbdms6IlGhchh8VceW9AqFtekpqYiSzbknn/gu6MahTBFMX5jJnzW6rYynlVcz6D6kwQbzlHMl11X/km0NJDXqeVpRmkpoYybPXDqJ1aBDPzt1qdRylPC41MZK7R6cw65Z0BnaK5J43V7Jk636rYynlFQrnvkin8pUEU8Ol9sU47LYGH521tHAbY+YZY8ZbmaE5hYc6uHFYEnPW7mHL3nKr46jT4O9t05NaBNt5ZeIQOke35NaZ2awuLLU6klKWWj3vPdrNewAAm0CIOHk8rZzUxMgGPV/3uJvZpOGdCQu289y8bVZHUcoybcIczLgpjYiwYG6ctpzcokNWR1LKElnv/Zdec29hn70d2ENA7NiCgokbcEGDX0MLdzNr2zKY69IT+WjlTvKLdZlDFbg6tAnltVuGIsD1L2exp6zC6khKeYzJz2THE6NIW/Mw61ukEnHvEuTGT2D0n2Di7AaP4QYt3B5xy4jOBNltPK973SrAdY5uyYyb0ig7Ws31Ly+j9EiV1ZGUanZV2xbgmnYRnQ6uwImNXr/4K63CI93FesT9p1W0QQu3R7RrHcpVQxJ4b0UhO0uPWh1HKUv1iWvD1BtSyS8+wqTpyzlSpUvhKv9VdqCIktdvwV47p5NNhKDCzDN6TS3cHvLLc7pgDEydr3vdSg3rEs1TVw9gVUEpt7+2gmpdlEf5oV3bN1L6zEja1uzHKQ4QO2IPbvCc5CeihdtD4iJacNmgeN5YXsC+cu3bU2pMn1j+cWlf5m8u4jfvrMLlMlZHUqppFGSx/627aTljNJGuEraMmYX9ps8a1Z9dn6Amiqka4PaRXXgnp4CXF27nD2N7Wh1HKctdndaJA4er+M8Xm4gMC+ahn/VCRE79RKW8VUEWrmljiXJWYwT2nvcMvTIucj92hgX7GN3j9qCk6JZc3L8jr2bmU3JYT8pRCuCOkV24aXhnpi/J45lvdbIi5dsKZv8dcVYjAiJ2Ys2+Jn8PLdwedueorhypcjJt8XaroyjlFUSEB8f15NKBcTz+1WZmLdO17JXvcTmdZE65nYSi+RixYZqoP7s+eqjcw1Lat2ZM7w5MW5LHLWcnEx7qsDqSUpaz2YR/X96PsqPVPPjhWiLDghnbN9bqWEo1SMWRQ6x/7mrSDy1gWfTPGTx+MraCJe6i3USHx+vSPW4L3DW6K+UVNby6VPcslDrGYbfx7DWDGNQpknvfXMlinddc+YCDaz7j4GMDGXRoAcu6/pq0O17GnpTRqPHZDaWF2wJ94towqnsMLy3M1TGsStVRd17zyTqvufJy+5a+Sav3ribGuQ+XzcHQc8YituYvq1q4LXLX6BRKjlTz+rIdVkdRyqu0CXMw8+Y0Ilu65zXfpvOaKy+0MetrWn9xNwKIgM24IG+hR95bC7dFUhMjGdYliqkLcqmodlodRymv0j48lFdvds9rfsPLWewu0xkHlfdY8fl0On96FYdohaldKIRmOhGtPlq4LXTX6K7sK6/knZxCq6Mo5XXqzmt+w8tZOoRSWc7kZ7Lj6XEMWHoPeY4uBN25GFsjFwo5E1q4LZSRHMWgThFMmbdNp3xUqh515zW/aYbOa66s48xbimvaWDoVLwKxkfSLfxEZ07HRC4WcCS3cFhIR7h6dws7So3zw3U6r4yjlldzzmg9kVUEpt722gqoa/SNXedaRQ2XsnTUZO+5uTREhZE+OZXm0cFtsZPcYencM5/l523DqXM1K1WtMnw48cmlfFui85srD9u8poPB/59GhagdOCWqyhULOhE7AYjH3XndXbnttBZ+u2c3F/TtaHUkpr3RVWieKa+c1b9tS5zVXzawgi5Llb2NWv0uCOczqs55jQI8U95njzTSxSkNp4fYCF/TqQEq7Vjz77VbG943FZtMvI6Xqc8fILhw4XMXLi7bTtmUwvzo3xepITUJEXgHGA/uMMX3qeVyAJ4GxwBHgRmPMCs+mDCAFWbimjSPC5T4hsmDEvxhw3rXuxyws2MfooXIvYLMJd47qyqa95Xy1Ya/VcZTyWiLCn8b25OcD43jiq828luk3sw9OB8ac5PGLgJTay2TgeQ9kCliFnz+BOKsQALHTKeSw1ZF+RAu3lxjfL5bEqDD+NWcjz87dQk5+idWRlPJKNpvwr8v7MbpHOx78cC2/euM7n/+8GGMWAAdOsskEYKZxywQiREQnc29ixuVi6Yw/Eb9zDkakWRcKORNauL1EkN3GuL4dyN1/mMe/3My1L2X6/JeRUs3FYbdxY0YSALNX7eKaF/3+8xIHFNS5XVh7n2oiNdsXU/ifYWRsf4ac8HOpuX424uHx2Q2lhduLhDrsALgMVNe4yMwttjiRUt5p055y7n9n5fe3a5x+/3mp78SXek+tF5HJIpItItlFRUXNHMs/HNn4NbYZ40g4ugGn2Bl42W8J7nK2x8dnN5QWbi8yvGsM9toT04LsNtKToyxOpJR3McYwbfF2fvbMIqqcLhx2wS7gCPL7z0shkFDndjywq74NjTFTjTGDjTGDY2JiPBLOlxXtyuPoW7cixv13kB2w7VhibahT0LPKvUhqYiT/vbI/v3pzJVcOTiA1MdLqSEp5jX3lFfz2ndXM31zEqO4x/Pvy/uw4cITM3GLSk6P8/fMyG7hLRN4EhgJlxpjdFmfyedvXL6fF21fRyhzC2B2IcXl0zvHG0sLtZS4eEMermfks2rofY4yOU1UK+Gr9Xn7/3moOV9bwfxN6c116IiJCTOsQvyjYIvIGMBKIFpFC4CHAAWCMmQJ8hnso2Fbcw8EmWZPUTxRksWfeVNpt/Ygj0pJdl31Il0iHV4zRbggt3F7oqiGduP+dVWTmHiCji18f/lPqpI5U1fD3Tzfw+rId9IoN56mrB9C1XWurYzU5Y8zVp3jcAHd6KI5/K8jC+cpY2ruqMSJUjn+cLv2Gux/z8oJ9jPZxe6Fx/WIJDw3izeW6VrcKXGsKyxj/1CLeyNrBL89O5oM7h/ll0VaeY4xhxZyXsbmqEQERG22P5lkd67Rp4fZCoQ47lw6MY87aPbqUoQo4TpfhuXlbufS5xRypcjLr5qH8YWxPQoLsVkdTPmxZbjEXP7WA0MLFIHjtGO2G0EPlXuqqtE7MWJrP+9/t5OazOlsdRymP2Fl6lPveWsmy7QcY1zeWf1zah4iwYKtjKR+Xk1/Cf15+lbtss+llL2BXv7voGNPWJ/qz66OF20v1jA2nf0IEb2bt4KbhSXqSmvJ7s1ft4k8frMHlMjx2RX8uGxSn7V41ie3fzeW1oL8TQjVOIyyxDeLyEZdZHavR9FC5F7t6SAJb9h1ixQ6/nhFKBbjyimrue2slv3rjO7q2a8Vn94zg8tR4LdqqyWTY1xOMu1/bABn2DVZHOiNauL3Yz/p3pGWwnTeyCk69sVI+KDvvABc9uZAPV+7k3vNSeOeXGSRGtbQ6lvIzcQMuwNjcB5jFHkzcgAssTnRmtHB7sZYhQVw8II5PVu/iYEW11XGUajLVThdPfLmJK19Yigi8c9sw7j2vG0F2/UpSzSAhjYL0vwKQ2+8en+zXrks/JV7u6rQEKqpdfPTdTqujKNUk8vYf5oopS3nq261cOjCez341wi8mUVHeLSL1cgBCdyyGgiyL05wZLdxerm9cG3rFhvNGVgHG1LumgFI+wRjD28sLGPvUQnKLDvHMNQN5/Mr+tA51WB1NBYCIozswQPyBxTDjYp8u3lq4vZyIcHVaAut3H2TNzjKr4yjVKCWHq7hj1gp+995q+sW34fN7z2Z8v45Wx1KBJG8hULvMmrPq+9u+SAu3D5gwMI5Qh01PUlM+afHW/Yx5cgFfb9jLHy7qweu3pNMxooXVsVSgSRqBC5t7LVQfnXjlGC3cPiA81MG4vh2ZvXInhytrrI6jVINU1jj5x6frufalZbQKCeKDO4bzy3O6YLPpMC9lgYQ0NsRdgQCVPSZYneaMaOH2EdcMTeBwlZNPVte7BK9SXmXz3nIueXYJLy7czvXpiXxy9wj6xLWxOpYKcCHRiQAEr33bp/u5tXD7iEGdIklp14rX9XC58mLGGGYsyeNnTy9i38EKXp44mP+7pA8tgnWecWW9duI+T0hw+XQ/txZuHyEiXJXWiVUFpWzYfdDqOEr9RFF5JTdNX85Ds9eR0SWKz+89m3N7trc6llLfazXgEowBg/h0P7cWbh/y84FxBNttvJmly30q7/LNhr2M+d8Clmwr5m8TejPtxiHEtA6xOpZSP2JPGkaxRFJuC4cxj/rsRCxauH1IZMtgxvTpwAff7aSi2ml1HKU4WuXkwQ/XcPOMbNqFh/LJ3WdxQ4YuiqO8VEEWbSmjtasMPn9A+7iVZ1yVlsDBiho+W7Pb6igqwK3dWcb4pxfyWuYOJp+dzId3DiOlfWurYyl1YnkLEVwIYLSP+/SISISIvCsiG0Vkg4hkWJHDF2UkR5EUFcabepJas9H2eXIul2HK/G1c+txiDlc6mXXLUP44tichQXoCmvJySSNwiXumPmML0j7u0/Qk8LkxpgfQH/DtNdY86NhJall5B9i6r9zqOP5K2+cJ7Co9yrUvLePRORs5r2d7Pr93BMO7RlsdS6mGSUhj5/B/AFAcPcTiMI3n8cItIuHA2cDLAMaYKmNMqadz+LLLBsVjF3jgvTXk5Ota3U1J22f9cvJL+NUb33HeE/NZVVjKvy/vx3PXDiIiLNjqaEqdlqi4LgBE7/XdOcut2ONOBoqAaSLynYi8JCK6AO9p2HHgCAbIzi/h2pcytXg3LW2fx8nJL+HKF5Yye9UujlY7+c/l/bhycIKegKZ8Usv9qzCAYHx2LLcVhTsIGAQ8b4wZCBwGHjh+IxGZLCLZIpJdVFTk6YxeLTO3mGPrhFXVuMjMLbY0j585ZfsMtLa5dNt+nC53izMG1u3SeQSUD0sagXGXbZ8dy21F4S4ECo0xy2pvv4v7i/JHjDFTjTGDjTGDY2JiPBrQ26UnRxES5P6vE4T05CiLE/mVU7bPQGubGV2iCQ2ycWz/+pVF25kyfxuVNTokUfmghDTywvpTgw1z4SM+OZbb44XbGLMHKBCR7rV3nQus93QOX5aaGMmsW9LpGxdOcJCN3h3DrY7kN7R9/lRqYiSzbk3nNxd259lrBnJWSgyPztnImP8tZO7GfVbHU+r0FGSReHQtDlzwxR98so87yKL3vRuYJSLBQC4wyaIcPis1MZLfXNiDia9ksXDLfs7vpVNLNiFtn8dJTYwkNTESgHH9OjJv0z7+9sl6Jk1fzuge7fjz+F50jg7oUwG+JyLjgN5A6LH7jDF/sy6R+pG8hdiMy329poKd331OnI/tdVsyHMwYs7L2UGM/Y8wlxhg9u6oRhnWJIiLMwae6YliT0vZ5aiO7t+Pze87mwXE9ydp+gAv+O59/ztnAoQBfdlZEpgC/wP3HnwBXAImWhlI/ljQClz0Yp3H/B+3I/pwVuXutTnVadOY0H+aw27iwVwe+3rBPp0BVHhccZOOWEcl8+5tzuGRAHC/Mz2XUY/N4L6cQl8uc+gX80zBjzA1AiTHmr0AGkGBxJlVXQhof9H2ex2t+wSs1F5BhW0fIezdAdYXVyRpMC7ePG9cvlkOVNSzY7P9nNyvv1K51KP+5oj8f3jmcuIgW3P/OKi6bsoRVBaVWR7PC0dp/j4hIR6Aa6GxhHlWPzgNH8YrtUv7uvJE/Vt9Mz0PLKHzibKq/+YdP9Hlr4fZxGccOl+vc5cpiAxIieP/2YTx2RX8KDhzlkucW87t3V1FUXml1NE/6REQigP8AK4A84E0rA6mfOnaC7/0XdGfCzX9iYcdJxB3ZRNDCf+OaPt7ri7dVJ6epJuKw2xjTuwMfr9pFRbWTUIfOF62sY7MJl6fGc2Hv9jzz7VZeWbydOWv2cM95KUwcloTD7vf7Cv82xlQC74nIJ7hPUPOdY7ABpO4Jl/SKx+wW9+jumkoKPnmU+F++i9i8s716Zyp1Wsb2jeVwlZP5erhceYnWoQ7+MLYnX9x7NqlJkfz90w2M+d+CQOjSWXrsijGm0hhTVvc+5aWSRiBBoRixY0RI2PsNK564lLKS/VYnq5cWbj+Q0SWKyDCHLvWpvE5yTCumT0rjlRsH43QZbngli1tnZrOj+IjV0ZqUiHQQkVSghYgMFJFBtZeRQFgDnv+KiOwTkbUneHykiJSJyMray1+a9icIcAlpMHE2MvpPMGkOS5Pvpl/5Qg4/lUH+V8/Cwse96vC5Hir3Aw67jQv1cLnyYqN7tGd412heWZTH099u4bz/zufWEZ25Y2RXWob4xdfQhcCNQDzwRJ37DwJ/bMDzpwPPADNPss1CY8z4RuZTp5KQBglp2ICMGzLYmD2KqE9uJnbRHzEiEBSKTJztFTOt6R63nxjXTw+XK+8WEmTn9pFdmPubkYzvG8uzc7dx7uPz+WjlTozx7eFjxpgZxphRwI3GmFF1LhOMMe834PkLgAPNn1Q1VI/B59J62M0gtQuS1Bzl8OqPrY4FNLBwi8g3IjL2uPumNk8k1RgZye7D5Z+uDqzD5do2fU/78FCe+MUA3rt9GDGtQ7jnzZVc+cJS1u4sszpaU1gsIi+LyBwAEeklIjc30WtniMgqEZkjIr2b6DXVSYT2vBCCWmAQMOBc/hKr5r5jdawG73F3Bn4vIg/VuW9wM+RRjRRktzGmTwe+2bA30CZj0bbpo1ITI/nozuH867K+5BYd5mfPLOIP76+h+JBPDx+bBnwBdKy9vRm4twledwWQaIzpDzwNfHiiDQNt9bpmlZCGTJyNnPtn9pz/DPtt7eg//xbWPnkZNXP/ZVm/d0MLdynuxRbai8jHItKm+SKpxhrXtyOHq5zM2xRQH9ZStG36LJtN+MWQTnz7m5HcNLwzb2cXMOqxeUxfvJ0ap8vqeI0RbYx5G3ABGGNqgDP+S9oYc9AYc6j2+meAQ0SiT7BtQK1e1+wS0mDE/cSedT0df7uEdW3OoU/J19jnP2LZmO+GFm4xxtQYY+4A3gMWAe2aL5ZqjPTktrRtGRxoZ5dr2/QDbVo4+PP4Xnx+zwj6xUfw8MfrGfvUQpZs9c7hOCdxWESiwL3cs4ikA2fcB1B71rrUXk/D/d1dfKavq05PaIuW9B58DgZBAKmppHDOYx7P0dDCPeXYFWPMdNxnT37ZDHnUGQiqPbv868A6XK5t04+ktG/Nqzen8cL1qRytdnLNS8u4/bUcCg74zPCx+4DZQBcRWYz7LPG7T/UkEXkD93jv7iJSKCI3i8htInJb7SaXA2tFZBXwFHCV8fUz+nzVcWO+43d9wfL/XsGhg55bi0h84f9+8ODBJjs72+oYPmHRlv1c9/IyplyXypg+HayOA4CI5Bhj/LLfWdtm86modvLSwlyenbsNlzH88pwu3H5OF1oEN91wx+ZomyISBHTHvfjUJmNMdVO+/unQ9tlMCrIgbyE18eks//YD0na8xC5bLDLiPuIdByFpxBkPGztZ2/SLAZTqB8cOl3+6ZrfXFG6lGiPUYeeu0SlclhrPPz/byFPfbOHd7AL+NK4XY/t2oPbIsTdKA5Jwf78OEhGMMScbn618Te2Y7yAg4+bhrFsymtgvJxM5/zceGfOt47j9zLHD5QF4drnyU7FtWvDU1QN5+5cZtAkL5s7XV3DV1Ew27D5odbSfEJFXgceAs4AhtRe/PNqkftB72FhapN/6/ZhvV00lhzfPa7b308Lth8b3i+VIlZN5m/ZZHUWpJpPWuS2f3H0W/7i0D5v3ljPuqYX8+cO1lByusjpaXYOB4caYO4wxd9defmV1KNX8WvS+CMSGAapMEL9a0pLpi7fz7Nyt5OQ3bf+3Fm4/NLTzscPle6yOolSTstuEa4cmMvc3I7k+PZFZy/IZ9fg8Xs3Mx+nyivN11gLaRxWIEtKQdr0RR0v2Dn+YhRWdefjj9Tz2xSaufSmzSYu3Fm4/VHcylqNVerhc+Z+IsGD+OqEPn90zgp4dwvnzh2sZ//QiluVaM0Kqdg6B2UA0sF5EvhCR2cculoRSHmXyMzH71mGqD9N+8cP0cW1y3w9U17jIbMK2qYXbT43vq4fLlf/r0SGc128dynPXDuLg0Wp+MTWTu15fwa7So56O8hjuxUXCgEuAR4DHa+9r7+kwynMO7NtJ5qy/Ujr9SsS4ECBYavhL3wOEOmzYBRxBNtKTo5rsPfWscj+V1rktUbVnl1/UN9bqOEo1GxFhbN9YRnVvxwsLtvH8vG18vWEvd4zsyuSzkz2yWp4xZn5tFsex63XytWj2AMqjnDU1rFv4ATXZM+lzaDHp4iTfnki46zA2DHZ7MANG/IxZw1PIzC0mPTmK1MTIJnt/Ldx+6tjh8neyC3ny682clRLTpA1HKW/TItjOved14/LUeB75bANPfLWZt7MLuCYtAQOkJ0c322dARG4H7gCSRWR1nYdaA4ub5U2Vx+3avpEd30ylc+GH9KOYEsJZ0eEKOoy8laSeg78f331sHHcqNEub08Ltx1LataLK6eJ/X2/h+fnbmHVLuhZv5ffiI8N47tpUlmzbz+/fXc2/v9iMTSA4aGtzfgZeB+YA/wQeqHN/uTFGl+v0YZVb51M093kq922jS/VmOhhhbYtUdvb/M31GX016SOgPG9eO725uWrj9WHllDfDjkyO0cKtAMaxLNBFhwRSUHMVlmvczYIwpwz0n+dVN/uLKEtvWZFLxzaP0Kp1LPO5VY9a0n0DMuD/Tr1OKpdm0cPuxYV2i+a9sxmWa/uQIpbzdnrIK1u8uw24TMEY/A+qUDpYWs+HLV2i7+S1SarZQY2zuiWsBETt9+/QHi4s2aOH2a6mJkdwxqivPfLuVv1/SR/e2VUB5ZfF2QHjm6oHk7j/c5CcIKf9gXC7WZ37OkWXT6FM6j6FSxXZbEpndf0fvPoNo/dEkcFYh9mB337UX0MLt5yZmJPHMt1vZe7DS6ihKeUzZkWpmZeYzvl+sjqpQP1WQxeHVH5NbuJOIPUvpbXZRblqwOvoiIoffTMqAEXS21Y6Wjpj9oxPOvIEWbj8X0zqEfvFtmLtxH3eO6mp1HKU84tXMPA5XObntnC5WR1FepKa6itzZ/6brmscJMy76CmwPSmZ5n0foc/4NDG3Z+qdP8tAJZ6dDC3cAGNm9Hc98u4WSw1VEtgy2Oo5Szaqi2sm0xXmM6h5Dz9hwq+MoL1CwZRWF375Eyu7ZdKMUA4iAERudz7mOziPutDriadGZ0wLAqO4xuAws2FJkdRSlmt072QUUH67Sve0Ad/RwOcs/fJb1/xhOwqyzGbLrNQrDerK5728hKBTEjthDvKbf+nToHncA6BcfQduWwczbVMSEAXFWx1Gq2dQ4XbywIJdBnSJI69zW6jjKw0x+JvszX2Pfzjw6lWUzRI5SKLEs7XwXKedPZkDHRPeGBRd4Xb/16dDCHQDsNuGcbjHM31yE02Xcw2OU8kOfrtlNYclRHvpZb0S0nQeCnPwS5m7cR1RxNhM330kMhmgDG1qlwVn30XPohcTbjju47IX91qdDD5UHiJHdYzhwuIrVhaVWR1GqWRhjeH7eNlLateLcHu2sjqM8ICe/hKumLuWZuVvZv24u7ummAIFebWroldIVOb5o+wH/+4lUvc5OicEmMHeT9nMr/zRvcxEb95Tzy3O6YNOjSgEhM7eYGqe7WC919abSBFODDRc2nLtXY54ZAm9cA/lLwXjFeu1NQg+VB4jIlsEM7BTJvE37uO/8blbHUarJPT9vGx3bhHJx/45WR1Eekp4cRYjDRnWNi3X2HnzY7zla71nG+wc6s+pwJDcEfcWNm7+izaZPKWvbn+Bz7qVFRCzsWOKz/dughTugjO7Rjv98sYmi8kpiWodYHUepJpOTf4Cs7Qf4y/heBAfpgcRAkZoYyaxb0o9bOvMKxhnDpr3lLNqSzm82TSJ+xwdM3P8pSR9Mqj2YLhibA9f1HxLUebi1P0QjaOEOICO7x/CfLzYxb9M+rhicYHUcpZrM8/NyiQhzcFWatutAk5oY+ZOpbEWEHh3C6dEhnFtGJFNRPZwV2x/g8Jd302v/FwgGcVVRNWMCa1oOpSpxJPGDxxKX3Nuin+L0aOEOIL1iw2nXOoR5m4q0cCu/sWVvOV9v2Ms956YQFqxfaeqnQh12hnVrDy1+AzMWYJxVGGzkthpCbPkmOqxfBOv/zi5pT0HkUBzdzqVL2jjatI2xOnq9tJUHEBFhVPd2fLZ2N9VOFw67HlJUvm/K/FxaOOxMHJZkdRTl7RLSYOJsJG8hkjSCXglpGJeLHVtXs3vFHIJ3LKB38Ve0ypyNc+m9bHakUNx+OOG9LyAldTTB+1Z7xfhvLdwBZlSPGN7KLmBFfglDdYlD5eN2lh7lo5U7uT4jkbY6na9qiOPGcIvNRqduA+jUbQDwB6qrKtn43TxK1n5J5O7FDCmcQdDOaVR84cAlTgSDsQcjN8xGEtMt+RG0cAeY4V2jCbIJczcVaeFWPu+lhbkA3DIi2eIkyl84gkPoMfRCGHoh4F6je1vWHKJXPkv8kfUIIM5KDk27hA0RI5Euo+icNo6o9vEey6iFO8C0DnUwJKkt8zbt44GLelgdR6lGKzlcxZtZBUwYEEdcRAur4yg/FR4RxcALroOe3WDGxRhnFS6EnaHd6Va6gDY5cyDnd2yzJ1MUk0HLXueTMvh8QsNaNVsmLdwBaFSPGB75bCO7So/SUb/wlI+asTSPo9VObjtH97aVB9TpH7cnjaB7QhrOmho2r17MgdVf0HrXQgbtfpPgPbOo+MbBmtA+HI4/m5j+Y+jceyg2ux0Kspqkj1wLdwAa1b0dj3y2kXmbirhmaCer4yh12o5U1TB9SR7n9WxPSvt61lBWqjkc1z9uDwqi26BzYNA5ABwuL2XD8i85uvFrOuxfSt9tT8K2Jznwfjh7w7rR7ehKbLjcq5JNnN3o4q2FOwB1bdeKuIgWfLtxnxZu5ZPezCqg9Eg1t4/UpTuV92jZOoL+o6+E0VcCsG/ndvKXf4rkzqXnwUXYqQGgpqaSeXPeY1v3KCqqnZyVEvOTsegno4U7AIkIo3rE8F7OTiprnIQE2a2OpFSDVTtdvLQwl7TObU/ry04pT2sX15l2cXcBd2HyMzHTxgAGpzh4YUcsy3M3AvD8/G3MuiW9we3ZkoG8IvJrEVknImtF5A0RCbUiRyAb3aMdR6udZG0/YHUUr6Pt07vNXrmLXWUV3H6O/+xti0iCiMwVkQ21be+eerYREXlKRLaKyGoRGWRFVtU4kpiOpJyPBLci96LXWSPdv3+susZFZm5xg1/L44VbROKAXwGDjTF9ADtwladzBLqM5GiCg2zM3airhdWl7dO7uVyGKfO30aNDa0Z2985ZrRqpBrjfGNMTSAfuFJFex21zEZBSe5kMPO/ZiOqMhcdB1SH+Ofs7IsOCCQmyYRdwBNlIP43huVYdKg8CWohINRAG7LIoR8BqEWwnIzmKz9fuJqpVcJ0J+hXaPr3W1IW5bNl3iHvPS0HEf5buNMbsBnbXXi8XkQ1AHLC+zmYTgJnGGANkikiEiMTWPld5OZOficmZiQ14KehfVFz2EVtCBh23QErDeHyP2xizE3gM2IG7oZYZY770dA4FXWNasqusgse/3MS1L2WSk19idSTLafv0Xjn5Jfzrc3ef4JT52/y2vYpIEjAQWHbcQ3FAQZ3bhbX3KS9XWXGEna/fhQ0nAA5xEb43k9TESO4c1fW0d5qsOFQeifsvx85AR6CliFxXz3aTRSRbRLKLivRwbnMQm3uPxWVOv4/FXzWkfWrbtMaH3+3EuNdk9Nv2KiKtgPeAe40xB49/uJ6nmHpeQ9unFykr3su2Jy4gvnILTrFjxI7Yg91juRvJipPTzgO2G2OKjDHVwPvAsOM3MsZMNcYMNsYMjonxq74sr3FR7w7fXz/dPhY/dsr2qW3TGjtLjgA0qk/QF4iIA3fRnmWMeb+eTQqBusv6xVNPN462T++xb+mbmKdTSalcT3bqv7Hf9Dky+k9nNIYbrOnj3gGki0gYcBQ4F8i2IEfAS01qy9ndosneXsKrN6dpH7ebtk8vVHqkisXbijm/V3sGJET43TkZ4u6wfxnYYIx54gSbzQbuEpE3gaG4u3G0f9tL5X3+NImZDwJg7MEMHjDwJxO4NJbHC7cxZpmIvAuswH0m5XfAVE/nUG7j+3Vkweb9tAp1WB3FK2j79E7v5hRSWePivvO70TM23Oo4zWE4cD2wRkRW1t73R6ATgDFmCvAZMBbYChwBJnk+pmqI7I9fYED2X0Dc/RtinO6pTptoKVBLzio3xjwEPGTFe6sfy6g93Lhk2366d9CpI0Hbp7dxuQyvZeYzJCnSX4s2xphF1N+HXXcbA9zpmUSqMYzLReb0B8jY8QK5jmQ6swuc1XCGfdrH05nTAlxC2zAS2rZg6bZiJg3vbHUcpX5i4db95BUf4dfnd7M6ilIntG7Jp4R88yAZzlyWt7mQ/nfMRPatbpJFRY6nhVuRkRzFF+v24nQZ7Db/GRur/MOrS/OJbhXMmD4dTr2xUhZYNfcd+sy7FbsYqo2d1sNuJTgktMn6tI9nyZSnyrtkdImi7Gg1G3YfP/pEKWsVlhzh2417+cWQBJ1TX3mlgi2rSJx/L7bakXmCoWTj3GZ9Ty3ciozkaACWbvO/cbHKt72RtQOAa4YmWpxEqZ9at+QzwmddhA0XVQRRY2xUE0Rkr9HN+r56qFzRoU0oydEtWZpbzK1nJ1sdRykAKmucvLW8gHN7ticuooXVcZT6QUEWBZ/+m267v2GXPY6g695hV/FOStZ/S2Sv0fQYcl6zvr0WbgVAepcoZq/cRY3TRZBdD8Qo632+dg/7D1VxfbrubSvv4cpfipk2jgScuESIuvwJWiX3hOSe0MwF+xj9hlYADOsSxaHKGtbsLLM6ilKA+6S0pKgwzuoabXUUpQCoOHKI3bNuw14757iIjVbFazyeQwu3Avh++sgl2s+tvMD6XQfJzi/huvREbDrSQXmB/XsK2PHEKGIr83BKUJPMOd5YeqhcARDdKoRu7VuRmVvMnaO6Wh1HBbjXluUT6rBxRWrCqTdWqjkVZHEg6w1Y8z7x5iirhj/LwJ7dmmV8dkNp4VbfG9YlmjeX76CqxkVwkB6MUdY4WFHNh9/t5OL+HWkTplPxKutsXPYlXT+/mkhXDQgUnvMfBo6uXSzQgoJ9jH47q++lJ0dRUe1iZUGp1VFUAHs/p5AjVU6uT0+yOooKRDVVsOVrCmfeSsJn1xFkahABg40ER7nV6QDd41Z1pCe3RcQ9njutc1ur46gAZIzh1cx8+idE0De+jdVxVKCoOkLVpq8oyXmPNgXfEOo8RIQJZYUrhaG2jdhwYewObBb0Z9dHC7f6XkRYML1iw1mau597SLE6jgpAS3OL2VZ0mMev6G91FOXnzNFSdmfPpnL1h3Tcv4gQU0mwacUnZjB5MaM4HDeCWSv20a96I8OCNjL2oivoYeHh8bq0cKsfyUiOYubSfCqqnYQ6dIpJ5VmvLs0nIszBuH6xVkdRfujgmk85tOQVqkp30/HoJjpSw14TwefB51KWNIZOg85jbJf2hAW7S+P41BIyc1NIT76RHl60/rsWbvUjw7pG8dKi7azIL2GYjp9VHrSnrIIv1+/llrM66x+NqklUVVawNedbytZ9Qfyer4iv2Um4gAvIaTWasn6T6DnkXCZEtqz3+amJkaR6UcE+Rgu3+pEhSW2x24SlucVauJVHvZG1A5cxXKvzkqtGMi4Xhbnr2JXzKSH580g5/B29pIIaY+OAPRpEAIOInSFDz4IRF1sduVG0cKsfaR3qoE9cG5ZsK+Z+q8OogFHtdPFG1g5GdouhU1SY1XGUDzlYWszWZZ9RvfkrEg5kkmD2kgDskvasjR6Do9v5dB16Ee3Kt8GMi8FZZdnEKU1FC7f6iWFdonhxQS6HK2toGaJNRDW/r9bvZV95JY9m6N62Ojln3lIOZL7GnpJygg5sIaVqI4PExSHTgi0tB1KQeDNxqeOI69KbjlJn1r2IKJg429KJU5qKfiurn8hIjuL5edtYnneAkd3bWR1HBYCZS/OIj2zBOd20vakf5OQVs2HdSvra8gjet5qwnUvoVLWZGCDaQEFQAsvjJ9Kmz4WkpI5mYHDIyV8wIc2nC/YxWrjVTwxOisRhd/dza+FWzW3L3nIycw/w+zE9sOu85IHL5cTs38zB7TmU5y6nZudKupVvIlWOAlBpgiiV8B+2t9npNHISnUYEXqeeFm71E2HBQQxIiCBTFxxRHvBaZj7BdhtXDo63OoryFGc1R3evp2jTMip2rCB0/1raHdlMqKmkDRBsgllvEpnvOou1pjPrXJ3JyBjOgwMqkJkT/KKf+kxo4Vb1ykiO4ulvt/LEV5s4p1s7rxwSoXzf4q37eSOrgIwubYlqdYrDnMq3FGRB3kKccWnsqgiidGsWrp0rCS9dR8fKXFpQTSfgkAllI0msDBvDkbZ9CE4YSIcufal02fjnazlUO104gmz8X/9OSKdIv+mnPhNauFW92rYKxgBPf7OVqQtymXVLuhZv1aSWbtvP9S8vw2Vgae4BcvJLtI35MGMM+/cWsGdzDrL5c3oWvo0NFzYDCQIJwEETxragLmyLuITq9v1plZRKp5S+DGrbisH1dJPMuiWdzNxi0pOjfmgbftJPfSa0cKt6lR2pBsAA1TUuMnOL9UtVNZniQ5Xc9/YqXMZ92+nUNuZLjhwqo3Dzd5RuX4lr7zpalW2mY2UuMRwkpnYbAwjgEmFHx4uoOOsBErv2ZmBww8uOt06AYjUt3KpeZ6XE8L9vtmAMOIJspCdHWR1J+YncokNMmr6c/YcqcdgFl8toG/NSzpoaduauZf+276jctZbQAxuJObKNjq49dBP3X11HTAiFjkS2Ro7AFdOLVp360SncTvjsm8FZhc0eTOJFv4YEnX++qWjhVvVKTYxkbJ8OfL5uLzNvStO/elWTyM47wC0zs7GJ8NYvMzCGnx4KVZ5T2w9N4lnsD45lz+YVHCpYhb1oAxHlW0ioyaeTuPuinUbYZYtlb1gKBVEXExLXl3ZdBtKxc0+62euZojZS+6KbixZudUJj+3bk0zV7CA7SeaPVmft09W5+/fZK4iJaMH3SEBKj3PNDa8H2nJz8EhZtKaJDm1Da7c/i7GWTseEEILr2ArCfCHaHdGZl9OXYOvQmsvMAEroNJCGsFQkNfTPti242WrjVCR37Qs3JL2FAQoS1YZTPMsYwdUEu/5yzkcGJkbx4w2AiWwZbHSvgZOcd4IopSzGAHSefB/8ZmzgRcfdH74gewcEBk+nYLZXodnHoSgXeSwu3OqEObUKJi2hBTv4Bbj6rs9VxlA+qcbp4+ON1vJa5g3H9Ynn8iv668pdFlm0/UHvN8HfHK6TYdmFsDjAuxB5Mpwl/0T1kH6GFW51UamIky7YXY4xBRGe1Ug13uLKGu9/4jm837uOX5yTz+wt7YNOZ0SyTnhzFUMdW7uYthtvXsbvfXcQOmaD90D5IC7c6qcFJkcxetYvCkqMktNVVm1TD7DtYwU0zlrN+10H+fkkfrkvXxUOslmrbwuuO/8PmqsaIndjBF2s/tI+yWR1AebdBndz93Ct2lFicRPmKzXvLufS5JeQWHealiYO1aHuLvIXYXO75GQQgf5GlcVTjaeFWJ9WjQ2taBtvJztPCrU5tybb9XPb8EqqcLt6anMHoHu2tjuQTRCRBROaKyAYRWSci99SzzUgRKRORlbWXv5zWm3w/r7dAAM/z7Q/0ULk6qSC7jQGdIsjJ18KtTu6D7wr53burSYpqybRJQ4iP1K6V01AD3G+MWSEirYEcEfnKGLP+uO0WGmPGN+odEtIguDW06wEXPqKHyH2Y7nGrU0rtFMnGPQc5VFljdRTlhYwxPPXNFn791ioGJ7bl3duHadE+TcaY3caYFbXXy4ENQJy1qZS30sKtTik1qS0uAyt3lFodRXmZaqeL37+3mie+2szPB8Yx46Y02rRwWB3Lp4lIEjAQWFbPwxkiskpE5ohI79N64YIsqCqHwmyYcbH7tvJJWrjVKQ3sFIEIerhc/Uh5RTU3TV/O29mF/OrcFB6/sj/BQfqVciZEpBXwHnCvMebgcQ+vABKNMf2Bp4EPT/Aak0UkW0Syi4qKfnggb2HtFQPOqjq3la/RT5k6pfBQB93btyY7/8CpN1YBYXfZUa6YspSl24r59+X9uO/8bjrO/wyJiAN30Z5ljHn/+MeNMQeNMYdqr38GOETkJxOcGWOmGmMGG2MGx8TE/PCAnpzmN/TkNNUggxIj+XjlLpwug10n0Qho63cdZNL0LA5XOpk2aQgjUmJO/SR1UuL+q+dlYIMx5okTbNMB2GuMMSKShnvHq7jBb5KQBkFhEBYJZ/9OT07zYbrHrRpkcGIk5ZU1bN5bbnUUZaH5m4u48oWl2ER49/YMLdpNZzhwPTC6znCvsSJym4jcVrvN5cBaEVkFPAVcZYwxDX6HgiyoOQIHd8HnD2gftw/TPW7VIHUXHOkZG25xGmWFt5bv4I8frKVb+9ZMu3EIHdqEWh3JbxhjFlE7L8pJtnkGeKbRb1JfH7fudfsk3eNWDdKpbRjRrUL0BLUAZIzhsS828fv31nBW12jeuS1Di7Yv0j5uv6F73KpBRITURJ2IJdBU1jj53bur+WjlLq5OS+BvE/rgsOvf+z5J+7j9hn4CVYMNTmzLjgNH2FdeYXUU5QFlR6q54eUsPlq5i99e2J1HLu2rRduXaR+339BPoWqwQbX93Ct0r9vvFRw4ws+fX8x3O0p58qoB3Dmqqw738nU6jttvaOFWDdYnLpzgIJseLvdzqwtLufS5xRSVVzLz5jQmDNCZN/2C9nH7jWYr3CLyiojsE5G1de5rKyJficiW2n8jm+v9VdMLCbLTL64N2X5QuLV91u/r9Xv5xQuZhDrsvH/HMNKTo6yOpJrKsT7u8I4w5lHt4/ZhzbnHPR0Yc9x9DwDfGGNSgG9qbysfkpoYydqdZVRUO62Ocqamo+3zR2YuzWPyq9mktG/FB3cMp2u71lZHUk1J+7j9RrMVbmPMAuD4OTInADNqr88ALmmu91fNIzUxkmqn4W8fr/fpQ+baPn+QnXeAK6Ys4S8frWN0j/a8OTmdmNYhVsdSTU37uP2Gp/u42xtjdoN7GTugnYffX52hILv7BKU3snZw7UuZPl286xFw7fPr9Xu58oWlLM8rwW4TfnlOMmHBOkrUL2kft9/w2pPTTrjCjbLUht3uKU8NUF3jIjO34VMl+wt/aJtOl2HGkjzumLUC17FJM40ha7suJOO3tI/bb3i6cO8VkViA2n/3nWjDE65woyyVnhzFsTVGHEE2fzt5qUHt09fb5prCMi55djEPzV5Hz9jWhATZsItf/n+qurSP2294+pjYbGAi8Gjtvx95+P3VGUpNjOT69ERmLM3n2WsGfT+HuZ/w6/Z5sKKaJ77czMyleUS1CuHpqwcyvl8sK3aUkplbTHpylL/9f6q6dK5yv9FshVtE3gBGAtEiUgg8hPsL8W0RuRnYAVzRXO+vms/YvrHMWJpvdYwzEkjt0xjDp2t287eP11N0qJIb0hO5/8LuhIc6APcfY1qwA4D2cfuNZivcxpirT/DQuc31nsoz+sS1wSawqrCMc3u2tzpOowRK+8wvPsxfPlrH/M1F9IkL56WJg+kXH2F1LGWFhDQIbg3tesCFj+jetg/T00fVaWsZEkTXdq1YXVhqdRR1ApU1Tl5ckMvT327FYbfx8M96cX1GEnabTluqlK/Twq0apV98BN9u3IcxRuew9jJLtxXz4Idr2FZ0mHH9YvnL+F60D9dlOANeQRZUlUNhNsy4GCbO1r1uH+W1w8GUd+sf34YDh6vYWXrU6iiq1v5Dldz39kqufjGTaqdh+qQhPHvNIC3ayk0nYPEbusetGuVYP+nqwjLiI8OsDRPgXC7DW9kFPDpnI0eqarhrVFfuGt2VUIfd6mjKm+jJaX5DC7dqlB6xrXHYhVWFpYztG2t1nIC1YfdB/vTBGlbsKGVo57b849I+Ose4qt+xCVjCIuHs3+lhch+mhVs1SkiQnZ6x4awuKLM6SkA6XFnDk99s4eVF22nTwsHjV/Tn54Pi9HwDdWLfT8By1D0BS/teWrx9lBZu1Wj94tvw0Xe7cLkMNj1b2WO+XLeHh2evY1dZBVenJfD7MT2ICAu2OpbydjoBi9/Qwq0arV9cBK9l7iB3/2G6tmtldRy/t7P0KA/PXsdX6/fSo0Nrnrp6IIOT2lodS/kK7eP2G1q4VaP1S2gDwOrCUi3czaja6WLa4u3896stAPzhoh7cdFZnHHYdFKJOg/Zx+w0t3KrRusa0ooXDzurCMn4+KN7qOH4pJ/8Af/pgLRv3lHNez/Y8fHEvPYtfNY72cfsNLdyq0YLsNvrEhesMas2g9EgV//p8I29kFdCxTShTr0/lgt4drI6lfJn2cfsNLdzqjPSLj+C1zHyqnS49dNsEjDG8v2Inj3y2gdKj1Uw+O5l7zk2hZYh+VNUZ0j5uv6HfBuqM9ItvQ2WNi817y+ndsY3VcXza1n2HePDDNWTmHmBQpwheu7QvPWPDrY6l/IX2cfsNLdzqjPSvM4OaFu7Gqah28uzcrUyZv42w4CD++fO+/GJwgg6xU01L+7j9hhZudUYSo8Jo08LB6sJSrk7rZHUcnzNv0z7+8tE6dhw4ws8HxfHHsT2JbhVidSzlj7SP229o4VZnREToF9+GVTqD2mnZe7CCv32ynk9X7yY5piWv3zqUYV2irY6l/Jn2cfsNLdzqjPWLb8OU+blUVDt1YYtTcLoMry7N47EvN1PtdHH/+d2YfE4yIUH6e1PNTPu4/YYWbnXG+sVH4HQZ1u06SGpipNVxvNbqwlL+9MFa1uwsY0RKNH+/pA+JUS2tjqUChfZx+w0t3OqM9Yv/YQY1Ldw/dbCimse/2MTMzHxiWoXwzDUDGdc3VhcEUZ6lfdx+Qwu3OmMdwkOJaR3CmkLt567LGMMnq3fzt0/Ws/9QJRMzkrjvgm6Ehzqsjqa8iIiEAguAENzfye8aYx46bhsBngTGAkeAG40xK07rjbSP229o4VZnTERIbBvGvM37yMkvCei97pz8EjJzi0mKCuOt7EIWbC6ib1wbXp44mH61Q+eUOk4lMNoYc0hEHMAiEZljjMmss81FQErtZSjwfO2/Dad93H5DC7c6Yzn5JawsKKXGZbj2xUxm3ZoekMU7J7+Ea1/KpLLahQFaOOz89eLeXJeeiF3HZKsTMMYY4FDtTUftxRy32QRgZu22mSISISKxxpjdDX4j7eP2GzpHpTpjmbnFOI37e6bK6SIzt9jiRNbIzC3+vmgD3Dg8iYnDkrRoq1MSEbuIrAT2AV8ZY5Ydt0kcUFDndmHtffW91mQRyRaR7KKioh8eqK+PW/kkLdzqjKUnRxFit2ETCA6ykZ4cZXUkS6QnRxHicP8eQh02zuvZ3upIykcYY5zGmAFAPJAmIn2O26S+v/6O3ys/9lpTjTGDjTGDY2JifnggaQTYQ0Ds2sft4/RQuTpjqYmRzLo1nczcYtKTowLyMDnU/h5u0d+DajxjTKmIzAPGAGvrPFQIJNS5HQ/sOq0XT0iDGz9x72knjdDD5D5MC7dqEqmJkVqo0N+DOn0iEgNU1xbtFsB5wL+O22w2cJeIvIn7pLSy0+rfPiYhTQu2H9DCrZRS1ooFZoiIHXf35dvGmE9E5DYAY8wU4DPcQ8G24h4ONsmqsMp6WriVUspCxpj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      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_theta = np.linspace(0,3, 60)\n",
    "phi_transformed = grid.transform(ds.phi, 'Z', target_theta, target_data=ds.theta, mask_edges=False)\n",
    "ax = plot_profile()\n",
    "phi_transformed.plot(ax=ax, y='theta', marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conservative transformation\n",
    "\n",
    "Conservative transformation is designed to preseve the total sum of `phi` over the `Z` axis.\n",
    "It presumes that `phi` is an _extensive quantity_, i.e. a quantity that is already volume weighted, with respect to the Z axis: for example, units of `Kelvins * meters` for heat content, rather than just `Kelvins`.\n",
    "The conservative method requires more input data at the moment.\n",
    "You have to not only specify the coordinates of the cell centers, but also the cell faces (or bounds/boundaries). In xgcm we achieve this by defining the bounding coordinates as the `outer` axis position.\n",
    "The target `theta` values are likewise intepreted as cell boundaries in `theta`-space.\n",
    "In this way, conservative transformation is similar to calculating a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (z: 10, zc: 11)\n",
       "Coordinates:\n",
       "  * z        (z) int64 2 3 4 5 6 7 8 9 10 11\n",
       "  * zc       (zc) float64 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5\n",
       "Data variables:\n",
       "    phi      (z) float64 1.85 1.289 1.342 1.711 ... 1.643 1.476 1.015 0.3637\n",
       "    theta    (z) float64 0.6931 1.099 1.386 1.609 ... 2.079 2.197 2.303 2.398</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1836ed0d-0be4-42ca-8729-ca0e08053fa7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1836ed0d-0be4-42ca-8729-ca0e08053fa7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 10</li><li><span class='xr-has-index'>zc</span>: 11</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-80d27f4d-17e8-4243-b64f-d75d8923a97f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80d27f4d-17e8-4243-b64f-d75d8923a97f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 9 10 11</div><input id='attrs-67dac45f-b346-4a2d-83a4-b8af6b18f525' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67dac45f-b346-4a2d-83a4-b8af6b18f525' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6732e1f4-d6ca-4dd8-bd97-954516065ae6' class='xr-var-data-in' type='checkbox'><label for='data-6732e1f4-d6ca-4dd8-bd97-954516065ae6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zc</span></div><div class='xr-var-dims'>(zc)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.5 3.5 4.5 ... 9.5 10.5 11.5</div><input id='attrs-ef824f01-1862-4ae4-bdb9-e9bcc2f065ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef824f01-1862-4ae4-bdb9-e9bcc2f065ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44728e2c-c7cd-4cbd-ad7d-995765932f03' class='xr-var-data-in' type='checkbox'><label for='data-44728e2c-c7cd-4cbd-ad7d-995765932f03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a9f2b60-4f05-42d2-b69b-229567c1c8e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a9f2b60-4f05-42d2-b69b-229567c1c8e6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>phi</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.85 1.289 1.342 ... 1.015 0.3637</div><input id='attrs-6b6a7100-50fc-436f-b160-bd81d43cb5a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b6a7100-50fc-436f-b160-bd81d43cb5a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13848adf-c79f-4bfd-bd75-9f74a38b866e' class='xr-var-data-in' type='checkbox'><label for='data-13848adf-c79f-4bfd-bd75-9f74a38b866e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.84972538, 1.28859294, 1.34248625, 1.71118461, 1.14369504,\n",
       "       1.83142081, 1.64334108, 1.47646734, 1.01490958, 0.36372929])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6931 1.099 1.386 ... 2.303 2.398</div><input id='attrs-4d13a5e6-d63f-441e-ba34-49b306b08a42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d13a5e6-d63f-441e-ba34-49b306b08a42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208207b7-a674-4209-95a7-49268f60b0a8' class='xr-var-data-in' type='checkbox'><label for='data-208207b7-a674-4209-95a7-49268f60b0a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.69314718, 1.09861229, 1.38629436, 1.60943791, 1.79175947,\n",
       "       1.94591015, 2.07944154, 2.19722458, 2.30258509, 2.39789527])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c39de486-27ab-45b8-a4e0-5540b2aa96a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c39de486-27ab-45b8-a4e0-5540b2aa96a4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (z: 10, zc: 11)\n",
       "Coordinates:\n",
       "  * z        (z) int64 2 3 4 5 6 7 8 9 10 11\n",
       "  * zc       (zc) float64 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5\n",
       "Data variables:\n",
       "    phi      (z) float64 1.85 1.289 1.342 1.711 ... 1.643 1.476 1.015 0.3637\n",
       "    theta    (z) float64 0.6931 1.099 1.386 1.609 ... 2.079 2.197 2.303 2.398"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the cell bounds in depth\n",
    "zc = np.arange(1,12)+0.5\n",
    "\n",
    "# add them to the existing dataset\n",
    "ds = ds.assign_coords({'zc': zc})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xgcm.Grid>\n",
       "Z Axis (not periodic, boundary=None):\n",
       "  * center   z --> outer\n",
       "  * outer    zc --> center"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recreate the grid object with a staggered `center`/`outer` coordinate layout\n",
    "grid = Grid(ds, coords={'Z':{'center':'z', 'outer':'zc'}},\n",
    "            periodic=False)\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently the `target_data`(`theta` in this case) has to be located on the `outer` coordinate for the conservative method (compared to the `center` for the linear method).\n",
    "\n",
    "We can easily interpolate `theta` on the outer coordinate with the grid object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;theta_outer&#x27; (zc: 11)&gt;\n",
       "array([0.34657359, 0.89587973, 1.24245332, 1.49786614, 1.70059869,\n",
       "       1.86883481, 2.01267585, 2.13833306, 2.24990484, 2.35024018,\n",
       "       1.19894764])\n",
       "Coordinates:\n",
       "  * zc       (zc) float64 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'theta_outer'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>zc</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-40ae9c79-a0bc-46b4-9482-2e7342fc0a5e' class='xr-array-in' type='checkbox' checked><label for='section-40ae9c79-a0bc-46b4-9482-2e7342fc0a5e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.3466 0.8959 1.242 1.498 1.701 1.869 2.013 2.138 2.25 2.35 1.199</span></div><div class='xr-array-data'><pre>array([0.34657359, 0.89587973, 1.24245332, 1.49786614, 1.70059869,\n",
       "       1.86883481, 2.01267585, 2.13833306, 2.24990484, 2.35024018,\n",
       "       1.19894764])</pre></div></div></li><li class='xr-section-item'><input id='section-271515a5-9d64-4341-9d74-40f43e01c0e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-271515a5-9d64-4341-9d74-40f43e01c0e0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zc</span></div><div class='xr-var-dims'>(zc)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.5 3.5 4.5 ... 9.5 10.5 11.5</div><input id='attrs-79dd379e-658c-46a3-a621-f4434546d722' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79dd379e-658c-46a3-a621-f4434546d722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809150f9-830a-455d-9d22-63cd2bbd8d0f' class='xr-var-data-in' type='checkbox'><label for='data-809150f9-830a-455d-9d22-63cd2bbd8d0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f8c1591-6bb2-42f1-9d87-3adb56c80c1b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f8c1591-6bb2-42f1-9d87-3adb56c80c1b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'theta_outer' (zc: 11)>\n",
       "array([0.34657359, 0.89587973, 1.24245332, 1.49786614, 1.70059869,\n",
       "       1.86883481, 2.01267585, 2.13833306, 2.24990484, 2.35024018,\n",
       "       1.19894764])\n",
       "Coordinates:\n",
       "  * zc       (zc) float64 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['theta_outer'] = grid.interp(ds.theta, 'Z', boundary='fill')\n",
    "ds['theta_outer']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets transform the data using the conservative method. Note that the target values will now be interpreted as cell bounds and not cell centers as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/transform.py:227: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
      "  out = xr.apply_ufunc(\n"
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       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;phi&#x27; (theta_outer: 19)&gt;\n",
       "array([0.        , 0.        , 0.42803042, 0.5316924 , 0.5316924 ,\n",
       "       0.5497499 , 0.58706736, 0.63919804, 0.87980119, 1.13800097,\n",
       "       1.32308247, 1.27698191, 2.07405321, 2.12604311, 1.58015893,\n",
       "       0.        , 0.        , 0.        , 0.        ])\n",
       "Coordinates:\n",
       "  * theta_outer  (theta_outer) float64 0.07895 0.2368 0.3947 ... 2.763 2.921</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'phi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>theta_outer</span>: 19</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-769eb8f1-c579-4fb6-b733-de3522975474' class='xr-array-in' type='checkbox' checked><label for='section-769eb8f1-c579-4fb6-b733-de3522975474' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.428 0.5317 0.5317 0.5497 ... 2.126 1.58 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0.        , 0.        , 0.42803042, 0.5316924 , 0.5316924 ,\n",
       "       0.5497499 , 0.58706736, 0.63919804, 0.87980119, 1.13800097,\n",
       "       1.32308247, 1.27698191, 2.07405321, 2.12604311, 1.58015893,\n",
       "       0.        , 0.        , 0.        , 0.        ])</pre></div></div></li><li class='xr-section-item'><input id='section-1c878f76-cb5e-4488-93b2-4cb33e9469a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c878f76-cb5e-4488-93b2-4cb33e9469a8' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta_outer</span></div><div class='xr-var-dims'>(theta_outer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07895 0.2368 ... 2.763 2.921</div><input id='attrs-3a52ce65-69eb-4f72-ae6e-772fc2a8f83b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a52ce65-69eb-4f72-ae6e-772fc2a8f83b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6b68c2a-6195-478b-aae9-d051eca2c04e' class='xr-var-data-in' type='checkbox'><label for='data-a6b68c2a-6195-478b-aae9-d051eca2c04e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.078947, 0.236842, 0.394737, 0.552632, 0.710526, 0.868421, 1.026316,\n",
       "       1.184211, 1.342105, 1.5     , 1.657895, 1.815789, 1.973684, 2.131579,\n",
       "       2.289474, 2.447368, 2.605263, 2.763158, 2.921053])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-682fda8c-2157-4aaa-aee9-669cd7bead6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-682fda8c-2157-4aaa-aee9-669cd7bead6a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'phi' (theta_outer: 19)>\n",
       "array([0.        , 0.        , 0.42803042, 0.5316924 , 0.5316924 ,\n",
       "       0.5497499 , 0.58706736, 0.63919804, 0.87980119, 1.13800097,\n",
       "       1.32308247, 1.27698191, 2.07405321, 2.12604311, 1.58015893,\n",
       "       0.        , 0.        , 0.        , 0.        ])\n",
       "Coordinates:\n",
       "  * theta_outer  (theta_outer) float64 0.07895 0.2368 0.3947 ... 2.763 2.921"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the target values in density\n",
    "theta_target = np.linspace(0,3, 20)\n",
    "\n",
    "# and transform\n",
    "phi_transformed_cons = grid.transform(ds.phi,\n",
    "                                      'Z',\n",
    "                                      theta_target,\n",
    "                                      method='conservative',\n",
    "                                      target_data=ds.theta_outer)\n",
    "phi_transformed_cons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c7020f3a0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "phi_transformed_cons.plot(y='theta_outer', marker='.', yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no point in comparing `phi_transformed_cons` directly to `phi` or the results of linear interoplation, since here we have reinterpreted `phi` as an extensive quantity.\n",
    "However, we can verify that the sum of the two quantities over the Z axis is exactly the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(13.66555232)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.phi.sum().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(13.66555232)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phi_transformed_cons.sum().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realistic Data Example\n",
    "\n",
    "To illustrate these features in a more realistic example, we use data from the [NCEP Global Ocean Data Assimilation](https://www.cpc.ncep.noaa.gov/products/GODAS/) (GODAS).\n",
    "This data are available from the [Pangeo Cloud Data Library](https://catalog.pangeo.io/browse/master/ocean/GODAS/).\n",
    "We can see that this is a full, global, 4D ocean dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-wrap {\n",
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       ".xr-text-repr-fallback {\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "\n",
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       "\n",
       ".xr-sections {\n",
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       "\n",
       ".xr-section-item input {\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
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       "\n",
       ".xr-section-summary {\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 417, lat_u: 417, level: 40, level_w: 40, lon: 360, lon_u: 360, time: 471)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 -74.16667 -73.83334 -73.50001 ... 64.16566 64.499\n",
       "  * lat_u      (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * level      (level) float32 5.0 15.0 25.0 35.0 ... 3483.0 3972.0 4478.0\n",
       "  * level_w    (level_w) float32 10.0 20.0 30.0 40.0 ... 3727.0 4225.0 4736.0\n",
       "  * lon        (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "  * lon_u      (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time       (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "Data variables:\n",
       "    dbss_obil  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    dbss_obml  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    dzdt       (time, level_w, lat, lon) float32 dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;\n",
       "    pottmp     (time, level, lat, lon) float32 dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;\n",
       "    salt       (time, level, lat, lon) float32 dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;\n",
       "    sltfl      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    sshg       (time, lat, lon) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    thflx      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    ucur       (time, level, lat_u, lon_u) float32 dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;\n",
       "    uflx       (time, lat_u, lon_u) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "    vcur       (time, level, lat_u, lon_u) float32 dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;\n",
       "    vflx       (time, lat_u, lon_u) float32 dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:      COARDS\n",
       "    References:       https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html\n",
       "    comment:          NOTE:  THESE ARE THE BIAS CORRECTED GODAS FILES.\n",
       "    creation_date:    Sat Dec 16 20:00:00 MDT 2006\n",
       "    dataset_title:    NCEP Global Ocean Data Assimilation System (GODAS)\n",
       "    grib_file:        godas.M.198001-12.grb\n",
       "    history:          Created 2006/12 by Hoop\n",
       "    html_BACKGROUND:  http://www.cpc.ncep.noaa.gov/products/GODAS/background....\n",
       "    html_GODAS:       www.cpc.ncep.noaa.gov/products/GODAS\n",
       "    html_REFERENCES:  http://www.cpc.ncep.noaa.gov/products/GODAS/background....\n",
       "    sfcHeatFlux:      \\nNote that the net surface heat flux are the total sur...\n",
       "    time_comment:     The internal time stamp indicates the FIRST day of the ...\n",
       "    title:            GODAS: Global Ocean Data Assimilation System</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3992a03b-b876-48e1-81b6-81f3cd0ca4db' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3992a03b-b876-48e1-81b6-81f3cd0ca4db' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 417</li><li><span class='xr-has-index'>lat_u</span>: 417</li><li><span class='xr-has-index'>level</span>: 40</li><li><span class='xr-has-index'>level_w</span>: 40</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>lon_u</span>: 360</li><li><span class='xr-has-index'>time</span>: 471</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-20aac27a-8e55-4996-986d-37ff4aadafc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20aac27a-8e55-4996-986d-37ff4aadafc3' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.16667 -73.83334 ... 64.499</div><input id='attrs-65d2b18a-e8bc-4602-8553-c9751009f357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65d2b18a-e8bc-4602-8553-c9751009f357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5abdcb99-fd04-46af-a73a-e7207ef7a469' class='xr-var-data-in' type='checkbox'><label for='data-5abdcb99-fd04-46af-a73a-e7207ef7a469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.5, 64.4990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.16667, -73.83334, -73.50001, ...,  63.83234,  64.16566,  64.499  ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_u</span></div><div class='xr-var-dims'>(lat_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.0 -73.66667 ... 64.66566</div><input id='attrs-8919e5a3-7b0d-498e-bcc1-04cbecabdd04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8919e5a3-7b0d-498e-bcc1-04cbecabdd04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bd0470-835a-410c-b342-429350fb2eab' class='xr-var-data-in' type='checkbox'><label for='data-20bd0470-835a-410c-b342-429350fb2eab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.0, 64.9990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.     , -73.66667, -73.33334, ...,  63.99901,  64.33234,  64.66566],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 3972.0 4478.0</div><input id='attrs-b30efa75-ec43-474f-8709-1447cf75475f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b30efa75-ec43-474f-8709-1447cf75475f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c201e83f-b351-48ff-a3b1-88f6c812f438' class='xr-var-data-in' type='checkbox'><label for='data-c201e83f-b351-48ff-a3b1-88f6c812f438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>actual_range :</span></dt><dd>[5.0, 4478.0]</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth below sea level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   5.,   15.,   25.,   35.,   45.,   55.,   65.,   75.,   85.,   95.,\n",
       "        105.,  115.,  125.,  135.,  145.,  155.,  165.,  175.,  185.,  195.,\n",
       "        205.,  215.,  225.,  238.,  262.,  303.,  366.,  459.,  584.,  747.,\n",
       "        949., 1193., 1479., 1807., 2174., 2579., 3016., 3483., 3972., 4478.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_w</span></div><div class='xr-var-dims'>(level_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.0 20.0 30.0 ... 4225.0 4736.0</div><input id='attrs-64a16ea4-5a3e-4d14-bdd2-ea5e4e33ac5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64a16ea4-5a3e-4d14-bdd2-ea5e4e33ac5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb1e5b25-4530-4314-9e22-bc1f6398de1f' class='xr-var-data-in' type='checkbox'><label for='data-bb1e5b25-4530-4314-9e22-bc1f6398de1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>actual_range :</span></dt><dd>[10.0, 4736.0]</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth below sea level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([  10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,   90.,  100.,\n",
       "        110.,  120.,  130.,  140.,  150.,  160.,  170.,  180.,  190.,  200.,\n",
       "        210.,  220.,  231.,  250.,  282.,  334.,  412.,  521.,  665.,  848.,\n",
       "       1071., 1336., 1643., 1990., 2376., 2797., 3249., 3727., 4225., 4736.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-152808ca-a1e1-4e93-b764-a474b5e49d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-152808ca-a1e1-4e93-b764-a474b5e49d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d6df3a-dbde-4113-b2f9-23f65028047a' class='xr-var-data-in' type='checkbox'><label for='data-b9d6df3a-dbde-4113-b2f9-23f65028047a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[0.5, 359.5]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_u</span></div><div class='xr-var-dims'>(lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-a901b40a-0b8e-41a6-b13a-6a90e59c7c70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a901b40a-0b8e-41a6-b13a-6a90e59c7c70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0d8f87d-7761-44ff-9963-768e38e039aa' class='xr-var-data-in' type='checkbox'><label for='data-c0d8f87d-7761-44ff-9963-768e38e039aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[1.0, 360.0]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-f73ab8d5-077e-4d34-855b-222b1522140d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f73ab8d5-077e-4d34-855b-222b1522140d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8ef05fa-10ea-4d3b-ace7-668080069638' class='xr-var-data-in' type='checkbox'><label for='data-a8ef05fa-10ea-4d3b-ace7-668080069638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19c38e61-9edd-4b06-b2c3-ca2f93180b8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19c38e61-9edd-4b06-b2c3-ca2f93180b8b' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dbss_obil</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;</div><input id='attrs-19c222e6-6df7-47cf-b79f-954ffec5c9ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19c222e6-6df7-47cf-b79f-954ffec5c9ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40bd1b4-4163-4ddf-b807-78dedc20c87a' class='xr-var-data-in' type='checkbox'><label for='data-e40bd1b4-4163-4ddf-b807-78dedc20c87a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>center :</span></dt><dd>US National Weather Service - NCEP (WMC)</dd><dt><span>dataset :</span></dt><dd>NCEP GODAS</dd><dt><span>gds_grid_type :</span></dt><dd>0</dd><dt><span>level_desc :</span></dt><dd>unknown</dd><dt><span>level_indicator :</span></dt><dd>238</dd><dt><span>long_name :</span></dt><dd>Geometric Depth Below Sea Surface</dd><dt><span>parameter_number :</span></dt><dd>195</dd><dt><span>parameter_table_version :</span></dt><dd>129</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>statistic :</span></dt><dd>Monthly Mean</dd><dt><span>sub_center :</span></dt><dd>Environmental Modeling Center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unpacked_valid_range :</span></dt><dd>[0.0, 5000.0]</dd><dt><span>valid_range :</span></dt><dd>[-16383, 16383]</dd><dt><span>var_desc :</span></dt><dd>ocean isothermal layer depth below sea surface</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbss_obml</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;</div><input id='attrs-dfd29b86-603a-4cf5-aa3a-909ac12b888b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd29b86-603a-4cf5-aa3a-909ac12b888b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36294c3-e044-46df-aaf4-19fa9ab54022' class='xr-var-data-in' type='checkbox'><label for='data-b36294c3-e044-46df-aaf4-19fa9ab54022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>center :</span></dt><dd>US National Weather Service - NCEP (WMC)</dd><dt><span>dataset :</span></dt><dd>NCEP GODAS</dd><dt><span>gds_grid_type :</span></dt><dd>0</dd><dt><span>level_desc :</span></dt><dd>unknown</dd><dt><span>level_indicator :</span></dt><dd>237</dd><dt><span>long_name :</span></dt><dd>Geometric Depth Below Sea Surface</dd><dt><span>parameter_number :</span></dt><dd>195</dd><dt><span>parameter_table_version :</span></dt><dd>129</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>statistic :</span></dt><dd>Monthly Mean</dd><dt><span>sub_center :</span></dt><dd>Environmental Modeling Center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unpacked_valid_range :</span></dt><dd>[0.0, 5000.0]</dd><dt><span>valid_range :</span></dt><dd>[-16383, 16383]</dd><dt><span>var_desc :</span></dt><dd>ocean mixed layer depth below sea surface</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vflx</span></div><div class='xr-var-dims'>(time, lat_u, lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 417, 360), meta=np.ndarray&gt;</div><input id='attrs-ee82da6a-2e34-4dca-a92c-9f07485dd664' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee82da6a-2e34-4dca-a92c-9f07485dd664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5c4c90d-ecb1-4a9c-9440-925cacf6409e' class='xr-var-data-in' type='checkbox'><label for='data-b5c4c90d-ecb1-4a9c-9440-925cacf6409e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>center :</span></dt><dd>US National Weather Service - NCEP (WMC)</dd><dt><span>dataset :</span></dt><dd>NCEP GODAS</dd><dt><span>gds_grid_type :</span></dt><dd>0</dd><dt><span>level_desc :</span></dt><dd>surface</dd><dt><span>level_indicator :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Momentum flux, v component</dd><dt><span>parameter_number :</span></dt><dd>125</dd><dt><span>parameter_table_version :</span></dt><dd>2</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>statistic :</span></dt><dd>Monthly Mean</dd><dt><span>sub_center :</span></dt><dd>Environmental Modeling Center</dd><dt><span>units :</span></dt><dd>N/m^2</dd><dt><span>unpacked_valid_range :</span></dt><dd>[-2.0, 2.0]</dd><dt><span>valid_range :</span></dt><dd>[-16384, 16384]</dd><dt><span>var_desc :</span></dt><dd>meridional momentum flux</dd></dl></div><div class='xr-var-data'><table>\n",
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       "Note that the net surface heat flux are the total surface heat flux \n",
       "from the NCEP reanalysis 2 plus the relaxation terms.</dd><dt><span>time_comment :</span></dt><dd>The internal time stamp indicates the FIRST day of the averaging period.</dd><dt><span>title :</span></dt><dd>GODAS: Global Ocean Data Assimilation System</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 417, lat_u: 417, level: 40, level_w: 40, lon: 360, lon_u: 360, time: 471)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 -74.16667 -73.83334 -73.50001 ... 64.16566 64.499\n",
       "  * lat_u      (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * level      (level) float32 5.0 15.0 25.0 35.0 ... 3483.0 3972.0 4478.0\n",
       "  * level_w    (level_w) float32 10.0 20.0 30.0 40.0 ... 3727.0 4225.0 4736.0\n",
       "  * lon        (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "  * lon_u      (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time       (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "Data variables:\n",
       "    dbss_obil  (time, lat, lon) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    dbss_obml  (time, lat, lon) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    dzdt       (time, level_w, lat, lon) float32 dask.array<chunksize=(4, 40, 417, 360), meta=np.ndarray>\n",
       "    pottmp     (time, level, lat, lon) float32 dask.array<chunksize=(4, 40, 417, 360), meta=np.ndarray>\n",
       "    salt       (time, level, lat, lon) float32 dask.array<chunksize=(4, 40, 417, 360), meta=np.ndarray>\n",
       "    sltfl      (time, lat, lon) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    sshg       (time, lat, lon) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    thflx      (time, lat, lon) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    ucur       (time, level, lat_u, lon_u) float32 dask.array<chunksize=(4, 40, 417, 360), meta=np.ndarray>\n",
       "    uflx       (time, lat_u, lon_u) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "    vcur       (time, level, lat_u, lon_u) float32 dask.array<chunksize=(4, 40, 417, 360), meta=np.ndarray>\n",
       "    vflx       (time, lat_u, lon_u) float32 dask.array<chunksize=(12, 417, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:      COARDS\n",
       "    References:       https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html\n",
       "    comment:          NOTE:  THESE ARE THE BIAS CORRECTED GODAS FILES.\n",
       "    creation_date:    Sat Dec 16 20:00:00 MDT 2006\n",
       "    dataset_title:    NCEP Global Ocean Data Assimilation System (GODAS)\n",
       "    grib_file:        godas.M.198001-12.grb\n",
       "    history:          Created 2006/12 by Hoop\n",
       "    html_BACKGROUND:  http://www.cpc.ncep.noaa.gov/products/GODAS/background....\n",
       "    html_GODAS:       www.cpc.ncep.noaa.gov/products/GODAS\n",
       "    html_REFERENCES:  http://www.cpc.ncep.noaa.gov/products/GODAS/background....\n",
       "    sfcHeatFlux:      \\nNote that the net surface heat flux are the total sur...\n",
       "    time_comment:     The internal time stamp indicates the FIRST day of the ...\n",
       "    title:            GODAS: Global Ocean Data Assimilation System"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from intake import open_catalog\n",
    "\n",
    "# cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml\")\n",
    "# ds  = cat[\"GODAS\"].to_dask()\n",
    "cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml\")\n",
    "ds  = cat[\"GODAS\"].to_dask()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid is missing an `outer` coordinate for the Z axis, so we will construct one.\n",
    "This will be needed for conservative interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_outer_data = np.pad(ds.level_w.values, [1, 0])\n",
    "level_outer = xr.DataArray(\n",
    "    level_outer_data,\n",
    "    dims=['level_outer'],\n",
    "    coords={'level_outer': ('level_outer', level_outer_data)}\n",
    ")\n",
    "ds = ds.assign_coords({'level_outer': level_outer})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a `Grid` object for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xgcm.Grid>\n",
       "Z Axis (not periodic, boundary=None):\n",
       "  * center   level --> outer\n",
       "  * outer    level_outer --> center\n",
       "X Axis (periodic, boundary=None):\n",
       "  * center   lon --> right\n",
       "  * right    lon_u --> center\n",
       "Y Axis (not periodic, boundary=None):\n",
       "  * center   lat --> right\n",
       "  * right    lat_u --> center"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = Grid(ds, coords={'Z': {'center': 'level', 'outer': 'level_outer'},\n",
    "                        'X': {'center': 'lon', 'right': 'lon_u'},\n",
    "                        'Y': {'center': 'lat', 'right': 'lat_u'}},\n",
    "            periodic=['X'])\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Interpolation\n",
    "\n",
    "To illustrate linear interpolation, we will interpolate salinity onto temperature surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to standard units\n",
    "theta = ds.pottmp - 273.15 \n",
    "salt = 1000 * ds.salt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
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       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "  display: block;\n",
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       "\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salt&#x27; (time: 471, lat: 417, lon: 360, pottmp: 38)&gt;\n",
       "dask.array&lt;transpose, shape=(471, 417, 360, 38), dtype=float64, chunksize=(4, 417, 360, 38), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -74.16667 -73.83334 -73.50001 ... 64.16566 64.499\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * pottmp   (pottmp) int64 -2 -1 0 1 2 3 4 5 6 7 ... 27 28 29 30 31 32 33 34 35</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'salt'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>lat</span>: 417</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>pottmp</span>: 38</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bf7e0363-4d12-4a49-bece-76043cedd98a' class='xr-array-in' type='checkbox' checked><label for='section-bf7e0363-4d12-4a49-bece-76043cedd98a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(4, 417, 360, 38), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 21.49 GB </td> <td> 182.55 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (471, 417, 360, 38) </td> <td> (4, 417, 360, 38) </td></tr>\n",
       "    <tr><th> Count </th><td> 1065 Tasks </td><td> 118 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-9257d02d-1b07-444b-8cf4-54498824613e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9257d02d-1b07-444b-8cf4-54498824613e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.16667 -73.83334 ... 64.499</div><input id='attrs-81075821-7e4a-47f8-8170-e15bed6283e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81075821-7e4a-47f8-8170-e15bed6283e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6240bba1-0bab-4efb-a7e7-95b71a28fe94' class='xr-var-data-in' type='checkbox'><label for='data-6240bba1-0bab-4efb-a7e7-95b71a28fe94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.5, 64.4990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.16667, -73.83334, -73.50001, ...,  63.83234,  64.16566,  64.499  ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-308f2301-7eaa-42b2-bc84-60c6600d6c34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-308f2301-7eaa-42b2-bc84-60c6600d6c34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61099bb8-cd58-41fb-9527-472c46902f89' class='xr-var-data-in' type='checkbox'><label for='data-61099bb8-cd58-41fb-9527-472c46902f89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[0.5, 359.5]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-730ebf97-e3a3-44e2-8330-fd448bb2ebf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-730ebf97-e3a3-44e2-8330-fd448bb2ebf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caf79a3a-8a20-4fca-9c0b-968e5208eddf' class='xr-var-data-in' type='checkbox'><label for='data-caf79a3a-8a20-4fca-9c0b-968e5208eddf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pottmp</span></div><div class='xr-var-dims'>(pottmp)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-2 -1 0 1 2 3 ... 30 31 32 33 34 35</div><input id='attrs-5f0de513-c682-4ea4-8bd6-b784b012bd27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f0de513-c682-4ea4-8bd6-b784b012bd27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd7889b-f85e-49dd-b76b-00b6eb086ae2' class='xr-var-data-in' type='checkbox'><label for='data-bbd7889b-f85e-49dd-b76b-00b6eb086ae2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2, -1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
       "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b7f8297-d760-4919-b8cd-bad1d953b376' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b7f8297-d760-4919-b8cd-bad1d953b376' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'salt' (time: 471, lat: 417, lon: 360, pottmp: 38)>\n",
       "dask.array<transpose, shape=(471, 417, 360, 38), dtype=float64, chunksize=(4, 417, 360, 38), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -74.16667 -73.83334 -73.50001 ... 64.16566 64.499\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * pottmp   (pottmp) int64 -2 -1 0 1 2 3 4 5 6 7 ... 27 28 29 30 31 32 33 34 35"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_theta_levels = np.arange(-2, 36)\n",
    "salt_on_theta = grid.transform(salt, 'Z', target_theta_levels, target_data=theta, method='linear')\n",
    "salt_on_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the computation is lazy. (No data has been downloaded or computed yet.)\n",
    "We can trigger computation by plotting something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/transform.py:60: RuntimeWarning: invalid value encountered in _interp_1d_linear\n",
      "  return _interp_1d_linear(phi, theta, target_theta_levels, mask_edges)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9c58402d60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salt_on_theta.isel(time=0).sel(pottmp=18).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/transform.py:60: RuntimeWarning: invalid value encountered in _interp_1d_linear\n",
      "  return _interp_1d_linear(phi, theta, target_theta_levels, mask_edges)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9c58d953d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salt_on_theta.isel(time=0).mean(dim='lon').plot(x='lat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conservative Interpolation\n",
    "\n",
    "To do conservative interpolation, we will attempt to calculate the meridional overturning in temperature space.\n",
    "Note that this is not a perfectly precise calculation, since the GODAS data are not exactly volume conserving as provided.\n",
    "However, it's sufficient to illustrate the basic principles of the calculation.\n",
    "\n",
    "To use conservative interpolation, we have to go from an intensive quantity (velocity) to an extensive one (velocity times cell thickness).\n",
    "We fill any missing values with 0, since they don't contribute to the transport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;v_transport&#x27; (time: 471, level: 40, lat_u: 417, lon_u: 360)&gt;\n",
       "dask.array&lt;where, shape=(471, 40, 417, 360), dtype=float32, chunksize=(4, 40, 417, 360), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * level    (level) float32 5.0 15.0 25.0 35.0 ... 3016.0 3483.0 3972.0 4478.0\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'v_transport'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>level</span>: 40</li><li><span class='xr-has-index'>lat_u</span>: 417</li><li><span class='xr-has-index'>lon_u</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bdecd1f0-3483-402f-9131-82a0f5ffcf7d' class='xr-array-in' type='checkbox' checked><label for='section-bdecd1f0-3483-402f-9131-82a0f5ffcf7d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(4, 40, 417, 360), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    <tr><th> Bytes </th><td> 11.31 GB </td> <td> 96.08 MB </td></tr>\n",
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       "\n",
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       "  <text x=\"258.020364\" y=\"148.402530\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"323.880237\" y=\"75.281511\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,323.880237,75.281511)\">417</text>\n",
       "  <text x=\"191.080246\" y=\"137.322284\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,191.080246,137.322284)\">40</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-af7d1d24-af1b-4753-8865-82a5aa343f51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af7d1d24-af1b-4753-8865-82a5aa343f51' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_u</span></div><div class='xr-var-dims'>(lat_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.0 -73.66667 ... 64.66566</div><input id='attrs-7de8f412-0ae4-4521-8551-1ab3c3761400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7de8f412-0ae4-4521-8551-1ab3c3761400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-242f90e4-2cae-4e0e-a7fc-d60b5722c61b' class='xr-var-data-in' type='checkbox'><label for='data-242f90e4-2cae-4e0e-a7fc-d60b5722c61b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.0, 64.9990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.     , -73.66667, -73.33334, ...,  63.99901,  64.33234,  64.66566],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 3972.0 4478.0</div><input id='attrs-7945c376-9770-425a-9ff0-5b77798e3c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7945c376-9770-425a-9ff0-5b77798e3c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbfb12ef-174a-4560-88e7-6f88d9a7f226' class='xr-var-data-in' type='checkbox'><label for='data-cbfb12ef-174a-4560-88e7-6f88d9a7f226' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>actual_range :</span></dt><dd>[5.0, 4478.0]</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth below sea level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   5.,   15.,   25.,   35.,   45.,   55.,   65.,   75.,   85.,   95.,\n",
       "        105.,  115.,  125.,  135.,  145.,  155.,  165.,  175.,  185.,  195.,\n",
       "        205.,  215.,  225.,  238.,  262.,  303.,  366.,  459.,  584.,  747.,\n",
       "        949., 1193., 1479., 1807., 2174., 2579., 3016., 3483., 3972., 4478.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_u</span></div><div class='xr-var-dims'>(lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-1a67f26a-e688-4bad-b1ad-422c113b0517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a67f26a-e688-4bad-b1ad-422c113b0517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e4ee76-f130-470f-be84-741ab01a64db' class='xr-var-data-in' type='checkbox'><label for='data-f6e4ee76-f130-470f-be84-741ab01a64db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[1.0, 360.0]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-7cc26be0-aea2-43f4-8d05-7865a5ab426c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cc26be0-aea2-43f4-8d05-7865a5ab426c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88f5cb53-3280-44f4-b405-23c4db72629a' class='xr-var-data-in' type='checkbox'><label for='data-88f5cb53-3280-44f4-b405-23c4db72629a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87fe6aaf-0109-4f1e-a045-fd49a763b79e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87fe6aaf-0109-4f1e-a045-fd49a763b79e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'v_transport' (time: 471, level: 40, lat_u: 417, lon_u: 360)>\n",
       "dask.array<where, shape=(471, 40, 417, 360), dtype=float32, chunksize=(4, 40, 417, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * level    (level) float32 5.0 15.0 25.0 35.0 ... 3016.0 3483.0 3972.0 4478.0\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thickness = grid.diff(ds.level_outer, 'Z')\n",
    "v_transport =  ds.vcur * thickness\n",
    "v_transport = v_transport.fillna(0.).rename('v_transport')\n",
    "v_transport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to interpolate `theta`, our target data for interoplation, to the same horizontal position as `v_transport`. This means moving from cell center to cell corner.\n",
    "This step introduces some considerable errors, particularly near the boundaries of bathymetry.\n",
    "(Xgcm currently has no special treatment for internal boundary conditions--see issue [222](https://github.com/xgcm/xgcm/issues/240).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;theta&#x27; (time: 471, level: 40, lat_u: 417, lon_u: 360)&gt;\n",
       "dask.array&lt;mul, shape=(471, 40, 417, 360), dtype=float32, chunksize=(4, 40, 416, 359), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * level    (level) float32 5.0 15.0 25.0 35.0 ... 3016.0 3483.0 3972.0 4478.0\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'theta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>level</span>: 40</li><li><span class='xr-has-index'>lat_u</span>: 417</li><li><span class='xr-has-index'>lon_u</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c53ac4bf-21c9-4ce9-8c30-595567f75dd9' class='xr-array-in' type='checkbox' checked><label for='section-c53ac4bf-21c9-4ce9-8c30-595567f75dd9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(4, 40, 416, 359), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    <tr><th> Bytes </th><td> 11.31 GB </td> <td> 95.58 MB </td></tr>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-131020f0-ede9-4581-b8c7-bb583f692e0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-131020f0-ede9-4581-b8c7-bb583f692e0a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-9a8d8365-d935-43e3-a02f-143288f26d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8d8365-d935-43e3-a02f-143288f26d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9069c441-effd-469f-8c2e-e87d67fa7021' class='xr-var-data-in' type='checkbox'><label for='data-9069c441-effd-469f-8c2e-e87d67fa7021' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 3972.0 4478.0</div><input id='attrs-08cdc84b-2b72-467b-9b73-f0eb2d292249' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08cdc84b-2b72-467b-9b73-f0eb2d292249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a03f2d18-6b24-4966-9e8f-856157a95fe3' class='xr-var-data-in' type='checkbox'><label for='data-a03f2d18-6b24-4966-9e8f-856157a95fe3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>actual_range :</span></dt><dd>[5.0, 4478.0]</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth below sea level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   5.,   15.,   25.,   35.,   45.,   55.,   65.,   75.,   85.,   95.,\n",
       "        105.,  115.,  125.,  135.,  145.,  155.,  165.,  175.,  185.,  195.,\n",
       "        205.,  215.,  225.,  238.,  262.,  303.,  366.,  459.,  584.,  747.,\n",
       "        949., 1193., 1479., 1807., 2174., 2579., 3016., 3483., 3972., 4478.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_u</span></div><div class='xr-var-dims'>(lat_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.0 -73.66667 ... 64.66566</div><input id='attrs-455bdde7-9bf0-48b2-8e38-4b7f37bd8ee6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-455bdde7-9bf0-48b2-8e38-4b7f37bd8ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed7068d6-2d98-453c-882b-4cb8db5172f1' class='xr-var-data-in' type='checkbox'><label for='data-ed7068d6-2d98-453c-882b-4cb8db5172f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.0, 64.9990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.     , -73.66667, -73.33334, ...,  63.99901,  64.33234,  64.66566],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_u</span></div><div class='xr-var-dims'>(lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-e2882293-6cef-493e-950b-62f6c9a01fe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2882293-6cef-493e-950b-62f6c9a01fe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7c089a1-a5b7-4c46-be9f-e4e5e9e18530' class='xr-var-data-in' type='checkbox'><label for='data-f7c089a1-a5b7-4c46-be9f-e4e5e9e18530' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[1.0, 360.0]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-331faf5a-c03f-4814-b0e7-baacbed3a8a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-331faf5a-c03f-4814-b0e7-baacbed3a8a2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'theta' (time: 471, level: 40, lat_u: 417, lon_u: 360)>\n",
       "dask.array<mul, shape=(471, 40, 417, 360), dtype=float32, chunksize=(4, 40, 416, 359), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * level    (level) float32 5.0 15.0 25.0 35.0 ... 3016.0 3483.0 3972.0 4478.0\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta = grid.interp(theta,['X', 'Y'], boundary='extend').rename('theta')\n",
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/grid.py:934: UserWarning: The `target data` input is not located on the cell bounds. This method will continue with linear interpolation with repeated boundary values. For most accurate results provide values on cell bounds.\n",
      "  warnings.warn(\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/transform.py:227: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
      "  out = xr.apply_ufunc(\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;v_transport&#x27; (time: 471, lat_u: 417, lon_u: 360, theta: 37)&gt;\n",
       "dask.array&lt;transpose, shape=(471, 417, 360, 37), dtype=float32, chunksize=(4, 416, 359, 37), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * theta    (theta) float64 -1.5 -0.5 0.5 1.5 2.5 ... 30.5 31.5 32.5 33.5 34.5</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'v_transport'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>lat_u</span>: 417</li><li><span class='xr-has-index'>lon_u</span>: 360</li><li><span class='xr-has-index'>theta</span>: 37</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3aea8497-3614-4b1c-8fdf-1dca8023bf62' class='xr-array-in' type='checkbox' checked><label for='section-3aea8497-3614-4b1c-8fdf-1dca8023bf62' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(4, 416, 359, 37), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 10.46 GB </td> <td> 88.41 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (471, 417, 360, 37) </td> <td> (4, 416, 359, 37) </td></tr>\n",
       "    <tr><th> Count </th><td> 19238 Tasks </td><td> 472 Chunks </td></tr>\n",
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       "  <text x=\"271.105441\" y=\"174.215062\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >37</text>\n",
       "  <text x=\"309.715565\" y=\"108.355189\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,309.715565,108.355189)\">360</text>\n",
       "  <text x=\"211.247658\" y=\"142.967404\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,211.247658,142.967404)\">417</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-0f3229bd-ee02-4856-9965-dfa788efbd64' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f3229bd-ee02-4856-9965-dfa788efbd64' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_u</span></div><div class='xr-var-dims'>(lat_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.0 -73.66667 ... 64.66566</div><input id='attrs-718a6a70-06ef-4b80-b031-70e6ad8b0ab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-718a6a70-06ef-4b80-b031-70e6ad8b0ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047caa08-01fb-4e78-8954-76c965ed384f' class='xr-var-data-in' type='checkbox'><label for='data-047caa08-01fb-4e78-8954-76c965ed384f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.0, 64.9990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.     , -73.66667, -73.33334, ...,  63.99901,  64.33234,  64.66566],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_u</span></div><div class='xr-var-dims'>(lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-02c4003f-1b39-486d-9c3f-6e91173b78fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02c4003f-1b39-486d-9c3f-6e91173b78fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97638a3-d972-49c9-8350-2eedbafcdf97' class='xr-var-data-in' type='checkbox'><label for='data-f97638a3-d972-49c9-8350-2eedbafcdf97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[1.0, 360.0]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-24f9d740-dd08-4262-b309-ae21f5b3b2c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f9d740-dd08-4262-b309-ae21f5b3b2c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d959b9d0-ae39-491f-bd01-46c4775811af' class='xr-var-data-in' type='checkbox'><label for='data-d959b9d0-ae39-491f-bd01-46c4775811af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta</span></div><div class='xr-var-dims'>(theta)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.5 -0.5 0.5 ... 32.5 33.5 34.5</div><input id='attrs-83f00442-d48e-4cc5-aee1-f83fc4549fb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f00442-d48e-4cc5-aee1-f83fc4549fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04bbf28b-77a2-4319-9521-3315154f7206' class='xr-var-data-in' type='checkbox'><label for='data-04bbf28b-77a2-4319-9521-3315154f7206' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.5, -0.5,  0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5,\n",
       "       10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5,\n",
       "       22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5,\n",
       "       34.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94c2d87f-85fc-41a3-b2d7-92b368898682' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-94c2d87f-85fc-41a3-b2d7-92b368898682' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'v_transport' (time: 471, lat_u: 417, lon_u: 360, theta: 37)>\n",
       "dask.array<transpose, shape=(471, 417, 360, 37), dtype=float32, chunksize=(4, 416, 359, 37), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat_u    (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u    (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * theta    (theta) float64 -1.5 -0.5 0.5 1.5 2.5 ... 30.5 31.5 32.5 33.5 34.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_transport_theta = grid.transform(v_transport, 'Z', target_theta_levels,\n",
    "                                   target_data=theta, method='conservative')\n",
    "v_transport_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this produced a warning. The `conservative` transformation method natively needs `target_data` to be provided on the cell bounds (here `level_outer`).\n",
    "Since transforming onto tracer coordinates is a very common scenario, xgcm uses linear interpolation to infer the values on the `outer` axis position.\n",
    "\n",
    "To demonstrate how to provide provide `target_data` on the outer grid position, we reproduce the steps xgcm executes internally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;theta&#x27; (time: 471, level_outer: 41, lat_u: 417, lon_u: 360)&gt;\n",
       "dask.array&lt;rechunk-merge, shape=(471, 41, 417, 360), dtype=float32, chunksize=(4, 41, 416, 359), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * level_outer  (level_outer) float32 0.0 10.0 20.0 ... 3727.0 4225.0 4736.0\n",
       "  * lat_u        (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u        (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'theta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>level_outer</span>: 41</li><li><span class='xr-has-index'>lat_u</span>: 417</li><li><span class='xr-has-index'>lon_u</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-efe65791-e1dc-4691-82c1-ad875adb18ce' class='xr-array-in' type='checkbox' checked><label for='section-efe65791-e1dc-4691-82c1-ad875adb18ce' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(4, 41, 416, 359), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 11.60 GB </td> <td> 97.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (471, 41, 417, 360) </td> <td> (4, 41, 416, 359) </td></tr>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-a2d25cf9-a6b2-43eb-a9fa-4e572b34cf7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2d25cf9-a6b2-43eb-a9fa-4e572b34cf7e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-03-01</div><input id='attrs-4ca9675b-df7f-4ba8-aea0-4d5a834acb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ca9675b-df7f-4ba8-aea0-4d5a834acb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a053bbe0-a9b9-4b0c-85e2-f66e83d30b9c' class='xr-var-data-in' type='checkbox'><label for='data-a053bbe0-a9b9-4b0c-85e2-f66e83d30b9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>info :</span></dt><dd>This is the FIRST day of the averaging period.</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_outer</span></div><div class='xr-var-dims'>(level_outer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 4225.0 4736.0</div><input id='attrs-12705d1a-4f55-4461-b535-6e39a17a1746' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12705d1a-4f55-4461-b535-6e39a17a1746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd12352b-4868-48eb-aa82-9c2975fb9f5d' class='xr-var-data-in' type='checkbox'><label for='data-fd12352b-4868-48eb-aa82-9c2975fb9f5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,   80.,   90.,\n",
       "        100.,  110.,  120.,  130.,  140.,  150.,  160.,  170.,  180.,  190.,\n",
       "        200.,  210.,  220.,  231.,  250.,  282.,  334.,  412.,  521.,  665.,\n",
       "        848., 1071., 1336., 1643., 1990., 2376., 2797., 3249., 3727., 4225.,\n",
       "       4736.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_u</span></div><div class='xr-var-dims'>(lat_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-74.0 -73.66667 ... 64.66566</div><input id='attrs-906fc6d8-b348-4695-819b-219ed2be6ae3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-906fc6d8-b348-4695-819b-219ed2be6ae3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf672a1-f734-4964-9dc5-2acf22d4ff82' class='xr-var-data-in' type='checkbox'><label for='data-6cf672a1-f734-4964-9dc5-2acf22d4ff82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[-74.0, 64.9990005493164]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-74.     , -73.66667, -73.33334, ...,  63.99901,  64.33234,  64.66566],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_u</span></div><div class='xr-var-dims'>(lon_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-df73f9b3-bc61-4a0d-a5ff-c98c4bb8d555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df73f9b3-bc61-4a0d-a5ff-c98c4bb8d555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b6f90e-c774-4f71-a50d-e600751ce9a2' class='xr-var-data-in' type='checkbox'><label for='data-e2b6f90e-c774-4f71-a50d-e600751ce9a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GridType :</span></dt><dd>Cylindrical Equidistant Projection Grid</dd><dt><span>actual_range :</span></dt><dd>[1.0, 360.0]</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a40390c9-5c10-42a4-9c1e-8f72c3a1ef9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a40390c9-5c10-42a4-9c1e-8f72c3a1ef9c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'theta' (time: 471, level_outer: 41, lat_u: 417, lon_u: 360)>\n",
       "dask.array<rechunk-merge, shape=(471, 41, 417, 360), dtype=float32, chunksize=(4, 41, 416, 359), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1980-01-01 1980-02-01 ... 2019-03-01\n",
       "  * level_outer  (level_outer) float32 0.0 10.0 20.0 ... 3727.0 4225.0 4736.0\n",
       "  * lat_u        (lat_u) float32 -74.0 -73.66667 -73.33334 ... 64.33234 64.66566\n",
       "  * lon_u        (lon_u) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_outer = grid.interp(theta,['Z'], boundary='extend')\n",
    "# the data cannot be chunked along the transformation axis\n",
    "theta_outer = theta_outer.chunk({'level_outer': -1}).rename('theta')\n",
    "theta_outer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we apply the transformation we can see that the results in this case are equivalent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xgcm/transform.py:227: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
      "  out = xr.apply_ufunc(\n"
     ]
    }
   ],
   "source": [
    "v_transport_theta_manual = grid.transform(v_transport, 'Z', target_theta_levels,\n",
    "                                   target_data=theta_outer, method='conservative')\n",
    "\n",
    "# Warning: this step takes a long time to compute. We will only compare the first time value\n",
    "xr.testing.assert_allclose(v_transport_theta_manual.isel(time=0), v_transport_theta.isel(time=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now  we verify visually that the vertically integrated transport is conserved under this transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9c483fa0a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_transport.isel(time=0).sum(dim='level').plot(robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9c48379e50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_transport_theta.isel(time=0).sum(dim='theta').plot(robust=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we attempt to plot a crude meridional overturning streamfunction for a single timestep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f9c58486160>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dx = 110e3 * np.cos(np.deg2rad(ds.lat_u))\n",
    "(v_transport_theta.isel(time=0) * dx).sum(dim='lon_u').cumsum(dim='theta').plot.contourf(x='lat_u', levels=31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance\n",
    "By default xgcm performs some simple checks when using `method='linear'`. \n",
    "It checks if the last value of the data is larger than the first, and if not, the data is flipped.This ensures that monotonically decreasing variables, like temperature are interpolated correctly. These checks have a performance penalty (~30% in some preliminary tests).\n",
    "\n",
    "If you have manually flipped your data and ensured that its monotonically increasing, you can switch the checks off to get even better performance.\n",
    "```python\n",
    "grid.transform(..., method='linear', bypass_checks=True)\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}